Dimple-7B

config.json

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+{
+  "_name_or_path": "/local_home2/yurunpeng/DvD/output/DiM-v0.5.3-Instruct-7B-autoreg-instruct_llava_next_instruct_lr_5e-07_wd_0.0_bs_128_tt_dlm_9fe0af92_seed_0_autoreg-recovery",
+  "architectures": [
+    "DimpleModel"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_dimple.DimpleConfig",
+    "AutoModel": "modeling_dimple.DimpleModel"
+  },
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "full_attn_mask": true,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "mask_token_id": 151666,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 28,
+  "model_type": "dimple",
+  "mrope_section": [
+    16,
+    24,
+    24
+  ],
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.2",
+  "use_cache": false,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "in_chans": 3,
+    "model_type": "dimple",
+    "spatial_patch_size": 14
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 152064
+}

configuration_dimple.py

@@ -0,0 +1,164 @@
+# coding=utf-8
+# Copyright 2024 The Dimple team and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Dimple model configuration"""
+
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+
+logger = logging.get_logger("Dimple."+__name__)
+
+
+class DimpleVisionConfig(PretrainedConfig):
+    model_type = "dimple"
+
+    def __init__(
+        self,
+        depth=32,
+        hidden_size=1280,
+        hidden_act="silu",
+        intermediate_size=3420,
+        num_heads=16,
+        in_channels=3,
+        patch_size=14,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        tokens_per_second=2,
+        window_size=112,
+        out_hidden_size=3584,
+        fullatt_block_indexes=[7, 15, 23, 31],
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.tokens_per_second = tokens_per_second
+        self.window_size = window_size
+        self.fullatt_block_indexes = fullatt_block_indexes
+        self.out_hidden_size = out_hidden_size
+
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        if config_dict.get("model_type") == "dimple":
+            config_dict = config_dict["vision_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class DimpleConfig(PretrainedConfig):
+    model_type = "dimple"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        image_token_id = 151655,
+        video_token_id = 151656,
+        vision_end_token_id = 151653,
+        vision_start_token_id = 151652,
+        vision_token_id = 151654,
+        rms_norm_eps=1e-6,
+        use_cache=False,  # cache not used in diffusion
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=28,
+        attention_dropout=0.0,
+        mask_token_id=151666,
+        pad_token_id=151643,
+        vision_config=None,
+        rope_scaling=None,
+        mrope_section=[16,24,24],
+        full_attn_mask = True,
+        **kwargs,
+    ):
+        if isinstance(vision_config, dict):
+            self.vision_config = DimpleVisionConfig(**vision_config)
+        elif vision_config is None:
+            self.vision_config = DimpleVisionConfig()
+
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window if use_sliding_window else None
+        self.max_window_layers = max_window_layers
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_dropout = attention_dropout
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self, ignore_keys={"mrope_section"})
+        self.mrope_section = mrope_section
+        
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        self.mask_token_id = mask_token_id
+        self.pad_token_id = pad_token_id
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_end_token_id = vision_end_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_token_id = vision_token_id
+        self.full_attn_mask = full_attn_mask

generation_config.json

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+{
+  "_from_model_config": true,
+  "alg": "origin",
+  "alg_p_threshold": null,
+  "alg_temp": null,
+  "bos_token_id": 151643,
+  "decoding_pipeline": "dim",
+  "eos_token_id": 151643,
+  "eps": 0.001,
+  "mask_token_id": null,
+  "output_history": false,
+  "pad_token_id": 151643,
+  "steps": 512,
+  "temperature": 0.0,
+  "top_k": null,
+  "top_p": null,
+  "transformers_version": "4.46.2",
+  "use_cache": false,
+  "use_original_confidence": true
+}

generation_utils.py

@@ -0,0 +1,707 @@
+# coding=utf-8
+# Copyright 2024 The Dimple team and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import warnings
+import copy
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+import torch.distributions as dists
+from torch.nn import functional as F
+from transformers import __version__
+from transformers.generation.configuration_utils import (
+    GenerationConfig,
+)
+from transformers.utils import (
+    ModelOutput,
+    is_torchdynamo_compiling,
+    logging,
+)
+from transformers.cache_utils import (
+    Cache,
+    DynamicCache,
+)
+from transformers.generation.utils import GenerationMixin
+
+logger = logging.get_logger("Dimple."+__name__)
+
+
+def top_p_logits(logits, top_p=None):
+    sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+    cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+    sorted_indices_to_remove = cumulative_probs > top_p
+    # Shift the indices to the right to keep the first token above the threshold
+    sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+    sorted_indices_to_remove[..., 0] = 0
+
+    mask = torch.zeros_like(logits, dtype=torch.bool, device=logits.device)
+    mask = mask.scatter_(-1, sorted_indices, sorted_indices_to_remove)
+    logits = logits.masked_fill(mask, torch.finfo(logits.dtype).min)
+    return logits
+
+def top_k_logits(logits, top_k=None):
+    top_k = min(top_k, logits.size(-1))  # Safety check
+    # Remove all tokens with a probability less than the last token of the top-k
+    indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+    logits = logits.masked_fill(indices_to_remove, torch.finfo(logits.dtype).min)
+    return logits
+
+
+def sample_tokens(logits, temperature=0.0, top_p=None, top_k=None, margin_confidence=False, neg_entropy=False, use_original_confidence = True):
+
+    if use_original_confidence:
+        original_logits = logits.clone()
+        original_probs = torch.softmax(original_logits, dim=-1)
+
+    if temperature > 0:
+        logits = logits / temperature
+    if top_p is not None and top_p < 1:
+        logits = top_p_logits(logits, top_p)
+    if top_k is not None:
+        logits = top_k_logits(logits, top_k)
+    probs = torch.softmax(logits, dim=-1)
+
+    if temperature > 0:
+        try:
+            x0 = dists.Categorical(probs=probs).sample()
+            if use_original_confidence:
+                confidence = torch.gather(original_probs, -1, x0.unsqueeze(-1)).squeeze(-1)
+            else:
+                confidence = torch.gather(probs, -1, x0.unsqueeze(-1)).squeeze(-1)
+        except:
+            if use_original_confidence:
+                _, x0 = probs.max(dim=-1)
+                confidence = torch.gather(original_probs, -1, x0.unsqueeze(-1)).squeeze(-1)
+            else:
+                confidence, x0 = probs.max(dim=-1)
+    else:
+        if use_original_confidence:
+            _, x0 = probs.max(dim=-1)
+            confidence = torch.gather(original_probs, -1, x0.unsqueeze(-1)).squeeze(-1)
+        else:
+            confidence, x0 = probs.max(dim=-1)
+    
+    if margin_confidence:
+        if use_original_confidence:
+            sorted_probs, _ = torch.sort(original_probs, dim=-1, descending=True)
+            # Extract top1 and top2 probabilities
+            top1_probs = sorted_probs[:, 0] 
+            top2_probs = sorted_probs[:, 1] 
+            # Calculate confidence as top1 - top2
+            confidence = top1_probs - top2_probs 
+        else:
+            sorted_probs, _ = torch.sort(probs, dim=-1, descending=True)
+            # Extract top1 and top2 probabilities
+            top1_probs = sorted_probs[:, 0] 
+            top2_probs = sorted_probs[:, 1] 
+            # Calculate confidence as top1 - top2
+            confidence = top1_probs - top2_probs 
+            
+    
+    if neg_entropy:
+        if use_original_confidence:
+            epsilon = 1e-10
+            log_probs = torch.log(original_probs + epsilon)
+            confidence = torch.sum(original_probs * log_probs, dim=-1)
+        else:
+            epsilon = 1e-10
+            log_probs = torch.log(probs + epsilon)
+            confidence = torch.sum(probs * log_probs, dim=-1)
+    
+    return confidence, x0
+
+
+@dataclass
+class DimpleModelOutput(ModelOutput):
+    sequences: torch.LongTensor = None
+    history: Optional[Tuple[torch.FloatTensor]] = None
+
+
+class DimpleGenerationConfig(GenerationConfig):
+    def __init__(self, **kwargs):
+        # cache parameter
+        self.use_cache: bool = kwargs.pop("use_cache", False)
+        # general generation parameter
+        self.temperature: float = kwargs.pop("temperature", 0.0)
+        self.top_p: Optional[float] = kwargs.pop("top_p", None)
+        self.top_k: Optional[int] = kwargs.pop("top_k", None)
+        self.max_length = kwargs.pop("max_length", 20)
+        self.max_new_tokens = kwargs.pop("max_new_tokens", None)
+        # diffusion specific params
+        self.eps: float = kwargs.pop("eps", 1e-3)
+        self.steps: int = kwargs.pop("steps", 512)
+        self.alg: str = kwargs.pop("alg", 'origin')
+        self.alg_temp: Optional[float] = kwargs.pop("alg_temp", None)
+        self.alg_p_threshold: Optional[float] = kwargs.pop("alg_p_threshold", None)
+        # highly recommended to be True!
+        self.use_original_confidence: Optional[bool] = kwargs.pop("use_original_confidence", True) 
+        # dim or dream.
+        self.decoding_pipeline: Optional[str] = kwargs.pop("decoding_pipeline", "dim") 
+
+        # Parameters that define the output variables of `generate`
+        self.num_return_sequences: int = kwargs.pop("num_return_sequences", 1)
+        self.return_dict_in_generate: bool = kwargs.pop("return_dict_in_generate", False)
+        self.output_history: bool = kwargs.pop("output_history", False)
+
+        # Special tokens that can be used at generation time
+        self.mask_token_id = kwargs.pop("mask_token_id", None)
+        self.pad_token_id = kwargs.pop("pad_token_id", None)
+        self.bos_token_id = kwargs.pop("bos_token_id", None)
+        self.eos_token_id = kwargs.pop("eos_token_id", None)
+
+        # Wild card
+        self.generation_kwargs = kwargs.pop("generation_kwargs", {})
+
+        # The remaining attributes do not parametrize `.generate()`, but are informative and/or used by the hub
+        # interface.
+        self._from_model_config = kwargs.pop("_from_model_config", False)
+        self._commit_hash = kwargs.pop("_commit_hash", None)
+        self.transformers_version = kwargs.pop("transformers_version", __version__)
+
+        # Additional attributes without default values
+        if not self._from_model_config:
+            # we don't want to copy values from the model config if we're initializing a `GenerationConfig` from a
+            # model's default configuration file
+            for key, value in kwargs.items():
+                try:
+                    setattr(self, key, value)
+                except AttributeError as err:
+                    logger.error(f"Can't set {key} with value {value} for {self}")
+                    raise err
+
+        # Validate the values of the attributes
+        self.validate(is_init=True)
+
+    def validate(self, is_init=False):
+        pass
+
+class DimpleGenerationMixin:
+    # in Dream
+
+    @staticmethod
+    def _expand_inputs_for_generation(
+        expand_size: int = 1,
+        is_encoder_decoder: bool = False,
+        input_ids: Optional[torch.LongTensor] = None,
+        **model_kwargs,
+    ) -> Tuple[torch.LongTensor, Dict[str, Any]]:
+        pixel_values = model_kwargs.get("pixel_values", None)
+        image_grid_thw = model_kwargs.get("image_grid_thw", None)
+        if expand_size == 1:
+            return GenerationMixin._expand_inputs_for_generation(
+                expand_size=expand_size,
+                is_encoder_decoder=is_encoder_decoder,
+                input_ids=input_ids,
+                **model_kwargs
+            )
+        elif pixel_values is None and image_grid_thw is None:
+            return GenerationMixin._expand_inputs_for_generation(
+                expand_size=expand_size,
+                is_encoder_decoder=is_encoder_decoder,
+                input_ids=input_ids,
+                **model_kwargs
+            )
+        else:
+            raise ValueError(
+                "Does not support expansion for image inputs. "
+            )
+
+    def _validate_generated_length(self, generation_config, input_ids_length, has_default_max_length):
+        """Performs validation related to the resulting generated length"""
+
+        # Can't throw warnings/exceptions during compilation
+        if is_torchdynamo_compiling():
+            return
+
+        # 1. Max length warnings related to poor parameterization
+        if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
+            # 20 is the default max_length of the generation config
+            logger.warning_once(
+                f"Using the model-agnostic default `max_length` (={generation_config.max_length}) to control the "
+                "generation length. We recommend setting `max_new_tokens` to control the maximum length of the "
+                "generation."
+            )
+        if input_ids_length >= generation_config.max_length:
+            input_ids_string = "input_ids"
+            raise ValueError(
+                f"Input length of {input_ids_string} is {input_ids_length}, but `max_length` is set to"
+                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
+                " increasing `max_length` or, better yet, setting `max_new_tokens`."
+            )
+
+    def _prepare_generated_length(
+        self,
+        generation_config,
+        has_default_max_length,
+        input_ids_length,
+    ):
+        """Prepared max and min length in generation configs to avoid clashes between similar attributes"""
+
+        if generation_config.max_new_tokens is not None:
+            if not has_default_max_length and generation_config.max_length is not None:
+                logger.warning_once(
+                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
+                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
+                    "Please refer to the documentation for more information. "
+                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
+                )
+            generation_config.max_length = generation_config.max_new_tokens + input_ids_length
+
+        elif has_default_max_length:
+            if generation_config.max_length == DimpleGenerationConfig().max_length:
+                generation_config.max_length = generation_config.max_length + input_ids_length
+                max_position_embeddings = getattr(self.config, "max_position_embeddings", None)
+                if max_position_embeddings is not None:
+                    generation_config.max_length = min(generation_config.max_length, max_position_embeddings)
+
+        return generation_config
+
+    def _prepare_generation_config(
+        self, generation_config: Optional[DimpleGenerationConfig], **kwargs: Dict
+    ) -> DimpleGenerationConfig:
+        """
+        Prepares the base generation config, then applies any generation configuration options from kwargs. This
+        function handles retrocompatibility with respect to configuration files.
+        """
+        # priority: `generation_config` argument > `model.generation_config` (the default generation config)
+        using_model_generation_config = False
+        if generation_config is None:
+            generation_config = DimpleGenerationConfig.from_model_config(self.config)
+            using_model_generation_config = True
+
+        # `torch.compile` can't compile `copy.deepcopy`, arguments in `kwargs` that are part of `generation_config`
+        # will mutate the object with `.update`. As such, passing these arguments through `kwargs` is disabled -- an
+        # exception will be raised in `_validate_model_kwargs`
+        if not is_torchdynamo_compiling():
+            generation_config = copy.deepcopy(generation_config)
+            model_kwargs = generation_config.update(**kwargs)
+            # If `generation_config` is provided, let's fallback ALL special tokens to the default values for the model
+            if not using_model_generation_config:
+                if generation_config.bos_token_id is None:
+                    generation_config.bos_token_id = self.generation_config.bos_token_id
+                if generation_config.eos_token_id is None:
+                    generation_config.eos_token_id = self.generation_config.eos_token_id
+                if generation_config.pad_token_id is None:
+                    generation_config.pad_token_id = self.generation_config.pad_token_id
+                if generation_config.mask_token_id is None:
+                    generation_config.mask_token_id = self.generation_config.mask_token_id
+
+        return generation_config, model_kwargs
+
+    def _prepare_special_tokens(
+        self,
+        generation_config: DimpleGenerationConfig,
+        device: Optional[Union[torch.device, str]] = None,
+    ):
+        """
+        Prepares the special tokens for generation, overwriting the generation config with their processed versions
+        converted to tensor.
+
+        Note that `generation_config` is changed in place and stops being serializable after this method is called.
+        That is no problem if called within `generate` (`generation_config` is a local copy that doesn't leave the
+        function). However, if called outside `generate`, consider creating a copy of `generation_config` first.
+        """
+
+        # Convert special tokens to tensors
+        def _tensor_or_none(token, device=None):
+            if token is None:
+                return token
+
+            device = device if device is not None else self.device
+            if isinstance(token, torch.Tensor):
+                return token.to(device)
+            return torch.tensor(token, device=device, dtype=torch.long)
+
+        bos_token_tensor = _tensor_or_none(generation_config.bos_token_id, device=device)
+        eos_token_tensor = _tensor_or_none(generation_config.eos_token_id, device=device)
+        pad_token_tensor = _tensor_or_none(generation_config.pad_token_id, device=device)
+        mask_token_tensor = _tensor_or_none(generation_config.mask_token_id, device=device)
+
+        # We can have more than one eos token. Always treat it as a 1D tensor (when it exists).
+        if eos_token_tensor is not None and eos_token_tensor.ndim == 0:
+            eos_token_tensor = eos_token_tensor.unsqueeze(0)
+
+        # Set pad token if unset (and there are conditions to do so)
+        if pad_token_tensor is None and eos_token_tensor is not None:
+            pad_token_tensor = eos_token_tensor[0]
+            logger.warning_once(f"Setting `pad_token_id` to `eos_token_id`:{pad_token_tensor} for open-end generation.")
+
+        # Update generation config with the updated special tokens tensors
+        # NOTE: this must be written into a different attribute name than the one holding the original special tokens
+        # (in their non-tensor form), in order to enable end-to-end compilation. See
+        # https://pytorch.org/docs/stable/torch.compiler_cudagraph_trees.html#limitations
+        generation_config._bos_token_tensor = bos_token_tensor
+        generation_config._eos_token_tensor = eos_token_tensor
+        generation_config._pad_token_tensor = pad_token_tensor
+        generation_config._mask_token_tensor = mask_token_tensor
+    def _mask_pad_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor,
+        generation_config: DimpleGenerationConfig,
+        **model_kwargs,
+    ) -> Tuple[torch.LongTensor, Dict[str, Any]]:
+        """
+        pad tokens in the input ids and attentions for generation. This is used to insert mask tokens into the input_ids
+        """
+        max_length = generation_config.max_length
+        mask_token_id = generation_config.mask_token_id
+        attention_mask = model_kwargs.get("attention_mask", None)
+
+        # pad input_ids to max_length
+        input_ids = F.pad(input_ids, (0, max_length - input_ids.shape[1]), value=mask_token_id)
+        if attention_mask is not None:
+            attention_mask = F.pad(attention_mask, (0, max_length - attention_mask.shape[1]), value=1.0)
+            model_kwargs["attention_mask"] = attention_mask
+        else:
+            raise ValueError(
+                "attention_mask should be provided. "
+            )
+
+        return input_ids, model_kwargs
+
+    def compare_past_key_values(self, old, new):
+        if len(old) != len(new):
+            return False
+        for (k_old, v_old), (k_new, v_new) in zip(old, new):
+            if not (torch.equal(k_old, k_new) and torch.equal(v_old, v_new)):
+                return False
+        return True
+
+    def _update_model_kwargs_for_generation(
+        self,
+        outputs: ModelOutput,
+        model_kwargs: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        # update past_key_values keeping its naming used in model code
+        if model_kwargs["use_cache"]:
+            assert outputs.past_key_values is not None, "Cache should not be None if use_cache is True"
+            assert outputs.past_key_values.get_seq_length() == model_kwargs["total_sequence_length"], \
+                f"Cache length {outputs.past_key_values.get_seq_length()} should be equal to the total sequence length {model_kwargs['total_sequence_length']}"
+            # The crop operation requires "left padding for batch processing"
+            outputs.past_key_values.crop(max_length = model_kwargs["prompt_length"])
+            # if model_kwargs["past_key_values"].get_seq_length() > 0:
+            #     assert self.compare_past_key_values(model_kwargs["past_key_values"], outputs.past_key_values), \
+            #         f"Cache {model_kwargs['past_key_values']} should be equal to the new cache {outputs.past_key_values}"
+        else:
+            assert outputs.past_key_values is None, "Cache should be None if use_cache is False"
+        model_kwargs["past_key_values"] = outputs.past_key_values
+
+        # update cache position
+        if model_kwargs["use_cache"]:
+            model_kwargs["cache_position"] = model_kwargs["cache_position"][-(model_kwargs["total_sequence_length"] - model_kwargs["prompt_length"]):]
+        else:
+            assert model_kwargs["cache_position"] is None, "Cache position should be None if use_cache is False"
+
+        if model_kwargs.get("rope_deltas", None) is not None:
+            assert torch.equal(
+                model_kwargs["rope_deltas"], outputs.rope_deltas), \
+                f"Rope deltas {model_kwargs['rope_deltas']} should be equal to the new rope deltas {outputs.rope_deltas}"
+        model_kwargs["rope_deltas"] = outputs.rope_deltas
+        return model_kwargs
+
+    @torch.no_grad()
+    def diffusion_generate(
+        self,
+        inputs: Optional[torch.Tensor] = None,
+        generation_config: Optional[DimpleGenerationConfig] = None,
+        # tokenizer=None, # only for debug, need to be removed !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+        **kwargs,
+    ) -> Union[DimpleModelOutput, torch.LongTensor]:
+        # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
+        generation_config, model_kwargs = self._prepare_generation_config(generation_config, **kwargs)
+        generation_tokens_hook_func = model_kwargs.pop("generation_tokens_hook_func", lambda step, x, logits: x)
+        generation_logits_hook_func = model_kwargs.pop("generation_logits_hook_func", lambda step, x, logits: logits)
+
+        # 2. Define model inputs
+        assert inputs is not None
+        input_ids = inputs
+        device = input_ids.device
+        attention_mask = model_kwargs.get("attention_mask", None)
+        self._prepare_special_tokens(generation_config, device=device)
+
+        # 3. Prepare `max_length`.
+        input_ids_length = input_ids.shape[-1]
+        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+        generation_config = self._prepare_generated_length(
+            generation_config=generation_config,
+            has_default_max_length=has_default_max_length,
+            input_ids_length=input_ids_length,
+        )
+
+        self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
+        
+        # 4. Check input_ids
+        if not is_torchdynamo_compiling() and self.device.type != input_ids.device.type:
+            logger.warning_once(
+                "You are calling .generate() with the `input_ids` being on a device type different"
+                f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
+                f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
+                " Please make sure that you have put `input_ids` to the"
+                f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
+                " running `.generate()`."
+            )
+        if (
+            hasattr(generation_config, "pad_token_id") and
+            torch.any(input_ids == generation_config.pad_token_id) and 
+            attention_mask is None
+        ):
+            logger.warning_once(
+                "Padding was detected but no attention mask is passed here. For correct "
+                "generation results, please set `attention_mask` when batch-padding inputs."
+            )
+
+        # 5. initialize kv cache
+        model_kwargs["use_cache"] = generation_config.use_cache
+        if model_kwargs["use_cache"]:
+            model_kwargs["past_key_values"] = DynamicCache()
+            model_kwargs["prompt_length"] = input_ids.shape[1] - 1
+        else:
+            model_kwargs["past_key_values"] = None
+            model_kwargs["prompt_length"] = input_ids.shape[1]  - 1
+
+        # 6. Expand inputs for generation
+        input_ids, model_kwargs = self._expand_inputs_for_generation(
+            input_ids=input_ids,
+            expand_size=generation_config.num_return_sequences,
+            is_encoder_decoder=self.config.is_encoder_decoder,
+            **model_kwargs,
+        )
+
+        # 7. pad mask for generation
+        input_ids, model_kwargs = self._mask_pad_inputs_for_generation(
+            input_ids=input_ids,
+            generation_config=generation_config,
+            **model_kwargs,
+        )
+        model_kwargs["total_sequence_length"] = input_ids.shape[1]               
+
+        # 8. initialize cache position
+        if model_kwargs["use_cache"]:
+            model_kwargs["cache_position"] = torch.ones_like(input_ids[0, :], dtype=torch.int64).cumsum(0) - 1
+        else:
+            model_kwargs["cache_position"] = None
+        # 9. Generate
+        result = self._sample(
+            input_ids,
+            generation_config=generation_config,
+            generation_tokens_hook_func=generation_tokens_hook_func,
+            generation_logits_hook_func=generation_logits_hook_func,
+            # tokenizer=tokenizer, # only for debug, need to be removed !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+            **model_kwargs,
+        )
+        return result
+
+    def _sample(
+        self,
+        input_ids: torch.LongTensor,
+        generation_config: DimpleGenerationConfig,
+        generation_tokens_hook_func,
+        generation_logits_hook_func,
+        # tokenizer=None, # only for debug, need to be removed !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+        **model_kwargs,
+    ) -> Union[DimpleModelOutput, torch.LongTensor]:
+        # init values
+        output_history = generation_config.output_history
+        return_dict_in_generate = generation_config.return_dict_in_generate
+        max_length = generation_config.max_length
+        mask_token_id = generation_config.mask_token_id
+        steps = generation_config.steps
+        eps = generation_config.eps
+        alg = generation_config.alg
+        alg_temp = generation_config.alg_temp
+        alg_p_threshold = generation_config.alg_p_threshold
+        decoding_pipeline = generation_config.decoding_pipeline
+        use_original_confidence = generation_config.use_original_confidence
+        temperature = generation_config.temperature
+        top_p = generation_config.top_p
+        top_k = generation_config.top_k
+        attention_mask = model_kwargs.get("attention_mask", None)
+        attention_mask_4d = model_kwargs.get("attention_mask_4d", None)
+
+        histories = [] if (return_dict_in_generate and output_history) else None
+
+        timesteps = torch.linspace(1, eps, steps + 1, device=input_ids.device)
+
+        input_ids = generation_tokens_hook_func(None, input_ids, None)
+
+        num_total_mask = (input_ids == mask_token_id).sum()
+
+        # this allows user-defined token control of the intermediate steps
+        for i in range(steps):
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+            x = model_inputs.pop("input_ids").clone()
+            mask_index = (x == mask_token_id)
+            outputs = self(x, **model_inputs)
+
+            model_kwargs = self._update_model_kwargs_for_generation(outputs, model_kwargs)
+            
+            logits = outputs.logits
+            assert torch.all(x[:,0] != mask_token_id), "The first token should not be a mask token"
+            logits = torch.cat([logits[:,:1], logits[:, :-1]], dim=1)
+
+            # this allows user-defined logits control of the intermediate steps
+            logits = generation_logits_hook_func(i, x, logits)
+
+            mask_logits = logits[mask_index]
+
+            if decoding_pipeline == 'dream':
+                # raise NotImplementedError("Dream decoding pipeline is copied from the original code.")
+                t = timesteps[i]
+                s = timesteps[i + 1]
+            
+                if alg == 'origin':
+                    p_transfer = 1 - s / t if i < steps - 1 else 1
+                    x0 = torch.zeros_like(x[mask_index], device=self.device, dtype=torch.long) + mask_token_id
+                    transfer_index_t_s = torch.rand(*x0.shape, device=self.device) < p_transfer
+                    _, x0[transfer_index_t_s]= sample_tokens(mask_logits[transfer_index_t_s], temperature=temperature, top_p=top_p, top_k=top_k, use_original_confidence = False)
+                    x[mask_index] = x0.clone()
+                elif alg == 'autoregressive':
+                    x0 = torch.zeros_like(x[mask_index], device=self.device, dtype=torch.long) + mask_token_id
+                    transfer_index_t_s = torch.zeros(*x.shape, device=self.device, dtype=torch.bool)
+                    transfer_index_t_s[torch.arange(x.shape[0]), mask_index.max(dim = 1)[1]] = True
+                    mask_transfer_index_t_s = transfer_index_t_s[mask_index]
+                    _, x0[mask_transfer_index_t_s]= sample_tokens(mask_logits[mask_transfer_index_t_s], temperature=temperature, top_p=top_p, top_k=top_k, use_original_confidence = False)
+                    x[mask_index] = x0.clone()
+                else:
+                    if alg == 'maskgit_plus':
+                        confidence, x0 = sample_tokens(mask_logits, temperature=temperature, top_p=top_p, top_k=top_k, use_original_confidence = False)
+                    elif alg == 'topk_margin':
+                        confidence, x0 = sample_tokens(mask_logits, temperature=temperature, top_p=top_p, top_k=top_k, margin_confidence=True, use_original_confidence = False)
+                    elif alg == 'entropy':
+                        confidence, x0 = sample_tokens(mask_logits, temperature, top_p=top_p, top_k=top_k, neg_entropy=True, use_original_confidence = False)
+                    else:
+                        raise RuntimeError(f"Unknown alg: {alg}")
+                    num_mask_token = mask_index.sum()
+                    number_transfer_tokens = int(num_mask_token * (1 - s / t)) if i < steps - 1 else num_mask_token
+                    if number_transfer_tokens > 0:
+                        if alg_temp is None or alg_temp == 0:
+                            _, transfer_index = torch.topk(confidence, number_transfer_tokens)
+                        else:
+                            confidence = confidence / alg_temp
+                            confidence = F.softmax(confidence, dim=-1)
+                            transfer_index = torch.multinomial(confidence, num_samples=number_transfer_tokens)
+                        x0_ = torch.zeros_like(x0, device=self.device, dtype=torch.long) + mask_token_id
+                        x0_[transfer_index] = x0[transfer_index].clone()
+                        x[mask_index] = x0_
+
+            elif decoding_pipeline == 'dim':
+
+                if alg == 'origin':
+                    confidence, x0= sample_tokens(mask_logits, temperature=temperature, top_p=top_p, top_k=top_k, use_original_confidence = use_original_confidence)
+                elif alg == 'origin-ratio':
+                    confidence, x0= sample_tokens(mask_logits, temperature=temperature, top_p=top_p, top_k=top_k, use_original_confidence = use_original_confidence)
+                elif alg == 'autoregressive':
+                    confidence, x0= sample_tokens(mask_logits, temperature=temperature, top_p=top_p, top_k=top_k, use_original_confidence = use_original_confidence)
+                elif alg == 'maskgit_plus':
+                    confidence, x0 = sample_tokens(mask_logits, temperature=temperature, top_p=top_p, top_k=top_k, use_original_confidence = use_original_confidence)
+                elif alg == 'topk_margin':
+                    confidence, x0 = sample_tokens(mask_logits, temperature=temperature, top_p=top_p, top_k=top_k, margin_confidence=True, use_original_confidence = use_original_confidence)
+                elif alg == 'entropy':
+                    confidence, x0 = sample_tokens(mask_logits, temperature, top_p=top_p, top_k=top_k, neg_entropy=True, use_original_confidence = use_original_confidence)
+                else:
+                    raise RuntimeError(f"Unknown alg: {alg}")
+
+                p_threshold_transfer_index_sum = 0
+                if alg_p_threshold is not None and alg_p_threshold > 0:
+                    if i == steps - 1:
+                        # all tokens should be transfered
+                        transfer_index = torch.ones_like(confidence, device=self.device, dtype=torch.bool)
+                    else:
+                        transfer_index = confidence > alg_p_threshold
+                    p_threshold_transfer_index_sum = transfer_index.sum()
+                    if p_threshold_transfer_index_sum == 0:
+                        pass
+                    else:
+                        x0_ = torch.zeros_like(x0, device=self.device, dtype=torch.long) + mask_token_id
+                        x0_[transfer_index] = x0[transfer_index].clone()
+                        x[mask_index] = x0_.clone()
+                else:
+                    pass
+
+                if p_threshold_transfer_index_sum == 0:
+
+                    num_cur_mask = mask_index.sum()
+                    ratio_cur_mask = num_cur_mask / num_total_mask
+                    if ratio_cur_mask <= timesteps[-1]:
+                        raise ValueError(f"ratio_cur_mask {ratio_cur_mask} should be larger than timesteps[-1] {timesteps[-1]}")
+                    
+                    valid_s_indices = (timesteps < ratio_cur_mask).nonzero(as_tuple=True)[0]
+                    s_idx_start = max(valid_s_indices.min().item(), i + 1)
+                    t = ratio_cur_mask
+                    for s_idx in range(s_idx_start, steps + 1):
+                        s = timesteps[s_idx]
+                        number_transfer_tokens = int(num_cur_mask * (1 - s / t)) if s > timesteps[-1] else num_cur_mask
+                        if number_transfer_tokens >= 1:
+                            break
+                        else:
+                            continue
+
+                    if alg == 'origin':
+                        x0_ = torch.zeros_like(x[mask_index], device=self.device, dtype=torch.long) + mask_token_id
+                        transfer_index_t_s = torch.randperm(x0_.shape[0])[:number_transfer_tokens]
+                        x0_[transfer_index_t_s]= x0[transfer_index_t_s].clone()
+                        x[mask_index] = x0_.clone()
+                    elif alg == 'origin-ratio':
+                        p_transfer = 1 - s / t if s > timesteps[-1] else 1
+                        x0_ = torch.zeros_like(x[mask_index], device=self.device, dtype=torch.long) + mask_token_id
+                        transfer_index_t_s = torch.rand(*x0_.shape, device=self.device) < p_transfer
+                        x0_[transfer_index_t_s]= x0[transfer_index_t_s].clone()
+                        x[mask_index] = x0_.clone()
+                    elif alg == 'autoregressive':
+                        x0_ = torch.zeros_like(x[mask_index], device=self.device, dtype=torch.long) + mask_token_id
+                        transfer_index_t_s = torch.zeros(*x.shape, device=self.device, dtype=torch.bool)
+                        transfer_index_t_s[torch.arange(x.shape[0]), mask_index.max(dim = 1)[1]] = True
+                        mask_transfer_index_t_s = transfer_index_t_s[mask_index]
+                        x0_[mask_transfer_index_t_s]= x0[mask_transfer_index_t_s].clone()
+                        x[mask_index] = x0_.clone()
+                    elif alg in ['maskgit_plus', 'topk_margin', 'entropy']:
+                        assert number_transfer_tokens > 0, f"number_transfer_tokens {number_transfer_tokens} should be larger than 0"
+                        if alg_temp is None or alg_temp == 0:
+                            _, transfer_index = torch.topk(confidence, number_transfer_tokens)
+                        else:
+                            confidence = confidence / alg_temp
+                            confidence = F.softmax(confidence, dim=-1)
+                            transfer_index = torch.multinomial(confidence, num_samples=number_transfer_tokens)
+                        x0_ = torch.zeros_like(x0, device=self.device, dtype=torch.long) + mask_token_id
+                        x0_[transfer_index] = x0[transfer_index].clone()
+                        x[mask_index] = x0_.clone()
+            else:
+                raise ValueError(f"Unknown decoding pipeline: {decoding_pipeline}")
+
+            # this allows user-defined token control of the intermediate steps
+            x = generation_tokens_hook_func(i, x, logits)
+
+            answer_token_length = model_kwargs['total_sequence_length'] - model_kwargs['prompt_length']
+            if x.shape[1] == model_kwargs['total_sequence_length']:
+                assert torch.all(x[:,:model_kwargs['prompt_length']+1] == input_ids[:,:model_kwargs['prompt_length']+1]), "prompt tokens should not be changed"
+            elif x.shape[1] == answer_token_length:
+                assert torch.all(
+                    x[:,0] == input_ids[:,-answer_token_length]), "The first token in x should be the same as the input_ids"
+            input_ids[:, -answer_token_length:] = x[:, -answer_token_length:].clone()
+
+            if histories is not None:
+                histories.append(input_ids.clone())
+
+            if decoding_pipeline == 'dim' and torch.all(input_ids != mask_token_id):
+                break
+        
+        if return_dict_in_generate:
+            return DimpleModelOutput(
+                sequences=input_ids,
+                history=histories,
+            )
+        else:
+            return input_ids

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+    "visual.blocks.9.mlp.up_proj.bias": "model-00001-of-00004.safetensors",
+    "visual.blocks.9.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
+    "visual.blocks.9.norm1.weight": "model-00001-of-00004.safetensors",
+    "visual.blocks.9.norm2.weight": "model-00001-of-00004.safetensors",
+    "visual.merger.ln_q.weight": "model-00001-of-00004.safetensors",
+    "visual.merger.mlp.0.bias": "model-00001-of-00004.safetensors",
+    "visual.merger.mlp.0.weight": "model-00001-of-00004.safetensors",
+    "visual.merger.mlp.2.bias": "model-00001-of-00004.safetensors",
+    "visual.merger.mlp.2.weight": "model-00001-of-00004.safetensors",
+    "visual.patch_embed.proj.weight": "model-00001-of-00004.safetensors"
+  }
+}

modeling_dimple.py

@@ -0,0 +1,1872 @@
+# coding=utf-8
+# Copyright 2024 The Dimple team and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Dimple model."""
+
+import math, os
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.nn import CrossEntropyLoss, LayerNorm
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, SlidingWindowCache, StaticCache, DynamicCache
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    ModelOutput,
+    BaseModelOutput,
+    MaskedLMOutput,
+)
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from transformers import PretrainedConfig
+
+from transformers.modeling_attn_mask_utils import (
+    AttentionMaskConverter,
+)
+
+from .configuration_dimple import DimpleConfig, DimpleVisionConfig
+from .generation_utils import DimpleGenerationMixin, DimpleGenerationConfig
+
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_varlen_func
+
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
+else:
+    flash_attn_varlen_func = None
+
+
+logger = logging.get_logger("Dimple."+__name__)
+
+_CHECKPOINT_FOR_DOC = "Dimple-7B"
+_CONFIG_FOR_DOC = "DimpleConfig"
+
+@dataclass
+class DimpleModelOutput(ModelOutput):
+    """
+    Base class for Dimple outputs.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+            `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+            The rope index difference between sequence length and multimodal rope.
+    """
+
+    logits: torch.FloatTensor = None
+    loss: Optional[torch.FloatTensor] = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    rope_deltas: Optional[torch.LongTensor] = None
+
+
+class DimpleRotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim=None,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+        rope_type="default",
+        config: Optional[DimpleConfig] = None,
+    ):
+        super().__init__()
+        # TODO (joao): remove the `if` below, only used for BC
+        self.rope_kwargs = {}
+        if config is None:
+            logger.warning_once(
+                "`DimpleRotaryEmbedding` can now be fully parameterized by passing the model config through the "
+                "`config` argument. All other arguments will be removed in v4.46"
+            )
+            self.rope_kwargs = {
+                "rope_type": rope_type,
+                "factor": scaling_factor,
+                "dim": dim,
+                "base": base,
+                "max_position_embeddings": max_position_embeddings,
+            }
+            self.rope_type = rope_type
+            self.max_seq_len_cached = max_position_embeddings
+            self.original_max_seq_len = max_position_embeddings
+        else:
+            # BC: "rope_type" was originally "type"
+            if config.rope_scaling is not None:
+                self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+            else:
+                self.rope_type = "default"
+            self.max_seq_len_cached = config.max_position_embeddings
+            self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    def reset_parameters(self):
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, self.inv_freq.device, **self.rope_kwargs)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+
+    def _dynamic_frequency_update(self, position_ids, device):
+        """
+        dynamic RoPE layers should recompute `inv_freq` in the following situations:
+        1 - growing beyond the cached sequence length (allow scaling)
+        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
+        """
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_seq_len_cached:  # growth
+            inv_freq, self.attention_scaling = self.rope_init_fn(
+                self.config, device, seq_len=seq_len, **self.rope_kwargs
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
+            self.max_seq_len_cached = seq_len
+
+        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+            self.max_seq_len_cached = self.original_max_seq_len
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        if "dynamic" in self.rope_type:
+            self._dynamic_frequency_update(position_ids, device=x.device)
+
+        # Core RoPE block. In contrast to other models, Dimple has different position ids for thw grids
+        # So we expand the inv_freq to shape (3, ...)
+        inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1)
+        position_ids_expanded = position_ids[:, :, None, :].float()  # shape (3, bs, 1, positions)
+        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+
+        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
+        cos = cos * self.attention_scaling
+        sin = sin * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2RMSNorm
+class DimpleRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        DimpleRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding with Multimodal Sections to the query and key tensors (https://qwenlm.github.io/blog/qwen2-vl/).
+
+    Explanation:
+        Multimodal 3D rotary position embedding is an extension to 1D rotary position embedding. The input embedding
+        sequence contains vision (images / videos) embedding and text embedding or just contains text embedding. For
+        vision embedding part, we apply rotary position embedding on temporal, height and width dimension seperately.
+        Here we split the channel dimension to 3 chunks for the temporal, height and width rotary position embedding.
+        For text embedding part, we just apply 1D rotary position embedding. The three rotary position index (temporal,
+        height and width) of text embedding is always the same, so the text embedding rotary position embedding has no
+        difference with modern LLMs.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        mrope_section(`List(int)`):
+            Multimodal rope section is for channel dimension of temporal, height and width in rope calculation.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    mrope_section = mrope_section * 2
+    cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
+        unsqueeze_dim
+    )
+    sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
+        unsqueeze_dim
+    )
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def apply_rotary_pos_emb_vision(
+    q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    orig_q_dtype = q.dtype
+    orig_k_dtype = k.dtype
+    q, k = q.float(), k.float()
+    cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float()
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    q_embed = q_embed.to(orig_q_dtype)
+    k_embed = k_embed.to(orig_k_dtype)
+    return q_embed, k_embed
+
+
+class DimpleVisionRotaryEmbedding(nn.Module):
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(seq, self.inv_freq)
+        return freqs
+
+
+class DimpleVisionPatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 14,
+        temporal_patch_size: int = 2,
+        in_channels: int = 3,
+        embed_dim: int = 1152,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.in_channels = in_channels
+        self.embed_dim = embed_dim
+
+        kernel_size = [temporal_patch_size, patch_size, patch_size]
+        self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        target_dtype = self.proj.weight.dtype
+        hidden_states = hidden_states.view(
+            -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size
+        )
+        hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim)
+        return hidden_states
+
+
+class DimplePatchMerger(nn.Module):
+    def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None:
+        super().__init__()
+        self.hidden_size = context_dim * (spatial_merge_size**2)
+        self.ln_q = DimpleRMSNorm(context_dim, eps=1e-6)
+        self.mlp = nn.Sequential(
+            nn.Linear(self.hidden_size, self.hidden_size),
+            nn.GELU(),
+            nn.Linear(self.hidden_size, dim),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.mlp(self.ln_q(x).view(-1, self.hidden_size))
+        return x
+
+class DimpleVisionMLP(nn.Module):
+    def __init__(self, config, bias: bool = False):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_state):
+        return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
+
+class DimpleVisionAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 16) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.qkv = nn.Linear(dim, dim * 3, bias=True)
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        seq_length = hidden_states.shape[0]
+        q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
+        if position_embeddings is None:
+            logger.warning_once(
+                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
+                "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed "
+                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be "
+                "removed and `position_embeddings` will be mandatory."
+            )
+            emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        else:
+            cos, sin = position_embeddings
+        q, k = apply_rotary_pos_emb_vision(q, k, cos, sin)
+
+        attention_mask = torch.full(
+            [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype
+        )
+        for i in range(1, len(cu_seqlens)):
+            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0
+
+        q = q.transpose(0, 1)
+        k = k.transpose(0, 1)
+        v = v.transpose(0, 1)
+        attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim)
+        attn_weights = attn_weights + attention_mask
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
+        attn_output = torch.matmul(attn_weights, v)
+        attn_output = attn_output.transpose(0, 1)
+        attn_output = attn_output.reshape(seq_length, -1)
+        attn_output = self.proj(attn_output)
+        return attn_output
+
+
+class DimpleVisionFlashAttention2(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 16) -> None:
+        raise NotImplementedError(
+            "FlashAttention2 is not supported in Dimple. Please use the `sdpa` implementation instead."
+        )
+        
+
+class DimpleVisionSdpaAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 16) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        self.qkv = nn.Linear(dim, dim * 3, bias=True)
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        seq_length = hidden_states.shape[0]
+        q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
+        if position_embeddings is None:
+            logger.warning_once(
+                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
+                "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed "
+                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be "
+                "removed and `position_embeddings` will be mandatory."
+            )
+            emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        else:
+            cos, sin = position_embeddings
+        q, k = apply_rotary_pos_emb_vision(q, k, cos, sin)
+
+        attention_mask = torch.zeros([1, seq_length, seq_length], device=q.device, dtype=torch.bool)
+        for i in range(1, len(cu_seqlens)):
+            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True
+        q = q.transpose(0, 1)
+        k = k.transpose(0, 1)
+        v = v.transpose(0, 1)
+        attn_output = F.scaled_dot_product_attention(
+            q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0), attention_mask, dropout_p=0.0
+        )
+        attn_output = attn_output.squeeze(0).transpose(0, 1)
+        attn_output = attn_output.reshape(seq_length, -1)
+        attn_output = self.proj(attn_output)
+        return attn_output
+
+
+DIMPLE_VISION_ATTENTION_CLASSES = {
+    "eager": DimpleVisionAttention,
+    # "flash_attention_2": DimpleVisionFlashAttention2,
+    "sdpa": DimpleVisionSdpaAttention,
+}
+
+
+class DimpleVisionBlock(nn.Module):
+    def __init__(self, config, attn_implementation: str = "sdpa") -> None:
+        super().__init__()
+        self.norm1 = DimpleRMSNorm(config.hidden_size, eps=1e-6)
+        self.norm2 = DimpleRMSNorm(config.hidden_size, eps=1e-6)
+        self.attn = DIMPLE_VISION_ATTENTION_CLASSES[attn_implementation](
+            config.hidden_size, num_heads=config.num_heads
+        )
+        self.mlp = DimpleVisionMLP(config, bias=True)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        hidden_states = hidden_states + self.attn(
+            self.norm1(hidden_states),
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb=rotary_pos_emb,
+            position_embeddings=position_embeddings,
+        )
+        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+        return hidden_states
+
+
+# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2MLP
+class DimpleMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_state):
+        return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class DimpleAttention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(self, config: DimpleConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = False # not used in Dream
+        self.attention_dropout = config.attention_dropout
+        self.rope_scaling = config.rope_scaling # in Dream rope scaling is None
+        self.mrope_section = config.mrope_section
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        
+        # implementaion in Qwen
+        # self.rotary_emb = DimpleRotaryEmbedding(
+        #     self.head_dim,
+        #     max_position_embeddings=self.max_position_embeddings,
+        #     base=self.rope_theta,
+        # )
+        # implementaion in dream, same if configs are same
+        self.rotary_emb = DimpleRotaryEmbedding(config=self.config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        # not used in Dream
+        # kv_seq_len = key_states.shape[-2]
+        # if past_key_value is not None:
+        #     kv_seq_len += cache_position[0] + 1
+
+        if position_embeddings is None:
+            logger.warning_once(
+                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
+                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
+                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
+                "removed and `position_embeddings` will be mandatory."
+            )
+            cos, sin = self.rotary_emb(value_states, position_ids)
+        else:
+            cos, sin = position_embeddings
+        query_states, key_states = apply_multimodal_rotary_pos_emb(
+            query_states, key_states, cos, sin, self.mrope_section
+        )
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # Fix precision issues in Dimple float16 inference
+        # Replace inf values with zeros in attention weights to prevent NaN propagation
+        if query_states.dtype == torch.float16:
+            attn_weights = torch.where(torch.isinf(attn_weights), torch.zeros_like(attn_weights), attn_weights)
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class DimpleFlashAttention2(DimpleAttention):
+
+    def __init__(self, *args, **kwargs):
+        raise NotImplementedError(
+            "DimpleFlashAttention2 is not implemented yet. Please use DimpleSdpaAttention instead."
+        )
+
+
+class DimpleSdpaAttention(DimpleAttention):
+    """
+    Dimple attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `DimpleAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from DimpleAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            Dimple.warning_once(
+                "DimpleModel is using DimpleSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                # cache_position=cache_position, # not used in Dream
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        # not used in Dream
+        # kv_seq_len = key_states.shape[-2]
+        # if past_key_value is not None:
+        #     kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        if position_embeddings is None:
+            logger.warning_once(
+                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
+                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
+                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
+                "removed and `position_embeddings` will be mandatory."
+            )
+            cos, sin = self.rotary_emb(value_states, position_ids)
+        else:
+            cos, sin = position_embeddings
+        query_states, key_states = apply_multimodal_rotary_pos_emb(
+            query_states, key_states, cos, sin, self.mrope_section
+        )
+
+        if past_key_value is not None:
+            logger.warning_once(
+                f"In {self.__class__}, cache is used."
+            )
+            # print("cache is used")
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # not used in Dream
+        # causal_mask = attention_mask
+        # if attention_mask is not None:  # no matter the length, we just slice it
+        #     causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+        # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+        # is_causal = True if causal_mask is None and q_len > 1 else False # not used in Dream
+
+        assert attention_mask.dtype == torch.bool, (
+            f"Attention mask should be of type `torch.bool`, but is {attention_mask.dtype}."
+        )
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask if isinstance(attention_mask, torch.Tensor) else None,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=False, # hard coded
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+DIMPLE_ATTENTION_CLASSES = {
+    "eager": DimpleAttention,
+    # "flash_attention_2": DimpleFlashAttention2,
+    "sdpa": DimpleSdpaAttention,
+}
+
+
+class DimpleDecoderLayer(nn.Module):
+    def __init__(self, config: DimpleConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        if config.sliding_window and config._attn_implementation != "flash_attention_2":
+            logger.warning_once(
+                f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
+                "unexpected results may be encountered."
+            )
+        # self.self_attn = Dimple_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+        self.self_attn = DimpleSdpaAttention(config, layer_idx)
+
+        self.mlp = DimpleMLP(config)
+        self.input_layernorm = DimpleRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = DimpleRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, sequence_length)` where padding elements are indicated by 0.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+                with `head_dim` being the embedding dimension of each attention head.
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+Dimple_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`DimpleConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Dimple Model outputting raw hidden-states without any specific head on top.",
+    Dimple_START_DOCSTRING,
+)
+class DimplePreTrainedModel(PreTrainedModel):
+    config_class = DimpleConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DimpleDecoderLayer", "DimpleVisionBlock"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, (nn.Linear, nn.Conv3d)):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        *model_args,
+        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
+        cache_dir: Optional[Union[str, os.PathLike]] = None,
+        ignore_mismatched_sizes: bool = False,
+        force_download: bool = False,
+        local_files_only: bool = False,
+        token: Optional[Union[str, bool]] = None,
+        revision: str = "main",
+        use_safetensors: Optional[bool] = None,
+        weights_only: bool = True,
+        **kwargs,
+    ):
+        _model = super().from_pretrained(
+            pretrained_model_name_or_path,
+            *model_args,
+            config=config,
+            cache_dir=cache_dir,
+            ignore_mismatched_sizes=ignore_mismatched_sizes,
+            force_download=force_download,
+            local_files_only=local_files_only,
+            token=token,
+            revision=revision,
+            use_safetensors=use_safetensors,
+            weights_only=weights_only,
+            **kwargs,
+        )
+        # NOTE(Lin): we need to override the generation config
+        # because the generation config loaded in `from_pretrained` 
+        # does not include all the attributes of DimpleGenerationConfig
+        resume_download = kwargs.get("resume_download", None)
+        proxies = kwargs.get("proxies", None)
+        subfolder = kwargs.get("subfolder", "")
+        from_auto_class = kwargs.get("_from_auto", False)
+        from_pipeline = kwargs.get("_from_pipeline", None)
+        _model.generation_config = DimpleGenerationConfig.from_pretrained(
+            pretrained_model_name_or_path,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            resume_download=resume_download,
+            proxies=proxies,
+            local_files_only=local_files_only,
+            token=token,
+            revision=revision,
+            subfolder=subfolder,
+            _from_auto=from_auto_class,
+            _from_pipeline=from_pipeline,
+        )
+        return _model
+
+
+class DimpleVisionTransformerPretrainedModel(DimplePreTrainedModel):
+    config_class = DimpleVisionConfig
+    _no_split_modules = ["DimpleVisionBlock"] 
+
+    def __init__(self, config, *inputs, **kwargs) -> None:
+        super().__init__(config, *inputs, **kwargs)
+        self.spatial_merge_size = config.spatial_merge_size
+        self.patch_size = config.patch_size
+        self.fullatt_block_indexes = config.fullatt_block_indexes
+        self.window_size = config.window_size
+        self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
+
+        self.patch_embed = DimpleVisionPatchEmbed(
+            patch_size=config.patch_size,
+            temporal_patch_size=config.temporal_patch_size,
+            in_channels=config.in_channels,
+            embed_dim=config.hidden_size,
+        )
+
+        head_dim = config.hidden_size // config.num_heads
+        self.rotary_pos_emb = DimpleVisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList(
+            [DimpleVisionBlock(config, config._attn_implementation) for _ in range(config.depth)]
+        )
+        self.merger = DimplePatchMerger(
+            dim=config.out_hidden_size,
+            context_dim=config.hidden_size,
+            spatial_merge_size=config.spatial_merge_size,
+        )
+        self.gradient_checkpointing = False
+
+    def get_dtype(self) -> torch.dtype:
+        return self.blocks[0].mlp.down_proj.weight.dtype
+
+    def get_device(self) -> torch.device:
+        return self.blocks[0].mlp.down_proj.weight.device
+
+    def rot_pos_emb(self, grid_thw):
+        pos_ids = []
+        for t, h, w in grid_thw:
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            hpos_ids = hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
+            hpos_ids = hpos_ids.flatten()
+
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            wpos_ids = wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
+            wpos_ids = wpos_ids.flatten()
+            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0)
+        max_grid_size = grid_thw[:, 1:].max()
+        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        return rotary_pos_emb
+
+    def get_window_index(self, grid_thw):
+        window_index: list = []
+        cu_window_seqlens: list = [0]
+        window_index_id = 0
+        vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size
+
+        for grid_t, grid_h, grid_w in grid_thw:
+            llm_grid_h, llm_grid_w = (
+                grid_h // self.spatial_merge_size,
+                grid_w // self.spatial_merge_size,
+            )
+            index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w)
+            pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
+            pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
+            num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
+            num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
+            index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100)
+            index_padded = index_padded.reshape(
+                grid_t,
+                num_windows_h,
+                vit_merger_window_size,
+                num_windows_w,
+                vit_merger_window_size,
+            )
+            index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
+                grid_t,
+                num_windows_h * num_windows_w,
+                vit_merger_window_size,
+                vit_merger_window_size,
+            )
+            seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
+            index_padded = index_padded.reshape(-1)
+            index_new = index_padded[index_padded != -100]
+            window_index.append(index_new + window_index_id)
+            cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1]
+            cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
+            window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()
+        window_index = torch.cat(window_index, dim=0)
+
+        return window_index, cu_window_seqlens
+
+    def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`):
+                The final hidden states of the model.
+            grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
+                The temporal, height and width of feature shape of each image in LLM.
+
+        Returns:
+            `torch.Tensor`: hidden_states.
+        """
+        hidden_states = self.patch_embed(hidden_states)
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        window_index, cu_window_seqlens = self.get_window_index(grid_thw)
+        cu_window_seqlens = torch.tensor(
+            cu_window_seqlens,
+            device=hidden_states.device,
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
+
+        seq_len, _ = hidden_states.size()
+        hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+        hidden_states = hidden_states[window_index, :, :]
+        hidden_states = hidden_states.reshape(seq_len, -1)
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+        rotary_pos_emb = rotary_pos_emb[window_index, :, :]
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+        position_embeddings = (emb.cos(), emb.sin())
+
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
+            dim=0,
+            # Select dtype based on the following factors:
+            #  - FA2 requires that cu_seqlens_q must have dtype int32
+            #  - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
+            # See https://github.com/huggingface/transformers/pull/34852 for more information
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        for layer_num, blk in enumerate(self.blocks):
+            if layer_num in self.fullatt_block_indexes:
+                cu_seqlens_now = cu_seqlens
+            else:
+                cu_seqlens_now = cu_window_seqlens
+            if self.gradient_checkpointing and self.training:
+                hidden_states = self._gradient_checkpointing_func(
+                    blk.__call__, hidden_states, cu_seqlens_now, None, position_embeddings
+                )
+            else:
+                hidden_states = blk(hidden_states, cu_seqlens=cu_seqlens_now, position_embeddings=position_embeddings)
+
+        hidden_states = self.merger(hidden_states)
+        reverse_indices = torch.argsort(window_index)
+        hidden_states = hidden_states[reverse_indices, :]
+
+        return hidden_states
+
+# Copied from transformers.models.llava.modeling_llava.LlavaMultiModalProjector with Llava->Dimple
+class DimpleMultiModalProjector(nn.Module):
+    def __init__(self, config: DimpleConfig):
+        super().__init__()
+
+        self.linear_1 = nn.Linear(config.vision_config.out_hidden_size, config.hidden_size, bias=True)
+        self.act = ACT2FN[config.hidden_act]
+        self.linear_2 = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+
+    def forward(self, image_features):
+        hidden_states = self.linear_1(image_features)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+@add_start_docstrings(
+    "The bare Dimple Model outputting raw hidden-states without any specific head on top.",
+    Dimple_START_DOCSTRING,
+)
+class DimpleBaseModel(DimplePreTrainedModel):
+    def __init__(self, config: DimpleConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [DimpleDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = DimpleRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = DimpleRotaryEmbedding(config=config)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            logger.warning_once(
+                    "This should not be triggered, in either training or inference, but if it is, please report it to us."
+                )
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        # the hard coded `3` is for temporal, height and width.
+        if position_ids is None:
+            logger.warning_once(
+                    "This should not be triggered, in either training or inference, but if it is, please report it to us."
+                )
+            position_ids = cache_position.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1)
+        elif position_ids.dim() == 2:
+            logger.warning_once(
+                    "This should not be triggered, in either training or inference, but if it is, please report it to us."
+                )
+            position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1)
+
+        # generate 4d caucasl attention mask
+        if len(attention_mask.shape) == 4 :
+            logger.warning_once(
+                f"4d Attention Mask is used."
+            )
+            if attention_mask.dtype != torch.bool:
+                logger.warning_once(
+                    f"Attention mask should be of type `torch.bool`, but is {attention_mask.dtype}. Changes have been made to convert it to `torch.bool`."
+                )
+                attention_mask = attention_mask.to(torch.bool)
+            pass
+        elif len(attention_mask.shape) == 2:
+            if not self.config.full_attn_mask:
+                # not used in Dream
+                diag_attention_mask = torch.arange(
+                        attention_mask.shape[1], 
+                        device=attention_mask.device
+                    ).unsqueeze(0) <= torch.arange(
+                        attention_mask.shape[1], 
+                        device=attention_mask.device
+                    ).unsqueeze(1)
+                diag_attention_mask = diag_attention_mask[None, None, :, :] # [1, 1, S, S]
+                pad_attention_mask = torch.logical_and(
+                        attention_mask.unsqueeze(1).unsqueeze(-2),
+                        attention_mask.unsqueeze(1).unsqueeze(-1),
+                    ) # bs, seq_len -> bs, 1, 1, seq_len; bs, 1, seq_len, 1 -> bs, 1, seq_len, seq_len
+                attention_mask = torch.logical_and(diag_attention_mask, pad_attention_mask)
+                attention_mask = attention_mask[:,:,cache_position] # [bs, 1, input_length, S]
+            else:
+                # used in Dream
+                attention_mask = torch.logical_and(
+                        attention_mask.unsqueeze(1).unsqueeze(-2),
+                        attention_mask.unsqueeze(1).unsqueeze(-1),
+                    ) # bs, seq_len -> bs, 1, 1, seq_len; bs, 1, seq_len, 1 -> bs, 1, seq_len, seq_len
+                eye = torch.eye(attention_mask.size(-1), dtype=torch.bool, device=attention_mask.device)  # [S, S]
+                attention_mask = torch.logical_or(attention_mask, eye.unsqueeze(0).unsqueeze(0))  # [bs, 1, S, S]
+                attention_mask = attention_mask[:,:,cache_position]  # [bs, 1, input_length, S]
+        else:
+            raise ValueError(f"Attention mask shape length must be 2 or 4, but got {len(attention_mask.shape)}")
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None # not used in Dream
+
+        # Dimple.debug(
+        #     f"In {self.__class__}, before input into decoder layers, Cache is {use_cache}, past_key_values is {past_key_values}, cache position is {cache_position}."
+        # )
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            # not used in Dream
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        # not used in Dream
+        next_cache = next_decoder_cache if use_cache else None
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+Dimple_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        pixel_values (`torch.FloatTensor` of shape `(seq_length, num_channels * image_size * image_size)):
+            The tensors corresponding to the input images. Pixel values can be obtained using
+            [`AutoImageProcessor`]. See [`DimpleImageProcessor.__call__`] for details. [`DimpleProcessor`] uses
+            [`DimpleImageProcessor`] for processing images.
+        pixel_values_videos (`torch.FloatTensor` of shape `(seq_length, num_channels * temporal_size * image_size * image_size)):
+            The tensors corresponding to the input videos. Pixel values can be obtained using
+            [`AutoImageProcessor`]. See [`DimpleImageProcessor.__call__`] for details. [`DimpleProcessor`] uses
+            [`DimpleImageProcessor`] for processing videos.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+            The temporal, height and width of feature shape of each video in LLM.
+        rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+            The rope index difference between sequence length and multimodal rope.
+"""
+
+
+class DimpleModel(DimpleGenerationMixin, DimplePreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.visual = DimpleVisionTransformerPretrainedModel._from_config(config.vision_config)
+        self.model = DimpleBaseModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.img_projector = DimpleMultiModalProjector(config)
+        # self.padding_side = "left"  # set it to left by default, user can use setter to change padding_sides # not specified in dream
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def reset_rope_parameters(self):
+        self.model.rotary_emb.reset_parameters()
+        for layer in self.model.layers:
+            layer.self_attn.rotary_emb.reset_parameters()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def get_rope_index(
+        self,
+        input_ids: torch.LongTensor,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Calculate the 3D rope index based on image and video's temporal, height and width in LLM.
+
+        Explanation:
+            Each embedding sequence contains vision embedding and text embedding or just contains text embedding.
+
+            For pure text embedding sequence, the rotary position embedding has no difference with mordern LLMs.
+            Examples:
+                input_ids: [T T T T T], here T is for text.
+                temporal position_ids: [0, 1, 2, 3, 4]
+                height position_ids: [0, 1, 2, 3, 4]
+                width position_ids: [0, 1, 2, 3, 4]
+
+            For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part
+            and 1D rotary position embeddin for text part.
+            Examples:
+                Assume we have a video input with 3 temporal patches, 2 height patches and 2 width patches.
+                input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision.
+                vision temporal position_ids: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2]
+                vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]
+                vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
+                text temporal position_ids: [3, 4, 5, 6, 7]
+                text height position_ids: [3, 4, 5, 6, 7]
+                text width position_ids: [3, 4, 5, 6, 7]
+                Here we calculate the text start position_ids as the max vision position_ids plus 1.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+                it.
+            image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+                The temporal, height and width of feature shape of each image in LLM.
+            video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+                The temporal, height and width of feature shape of each video in LLM.
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+        Returns:
+            position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`)
+            mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`)
+        """
+        spatial_merge_size = self.config.vision_config.spatial_merge_size
+        image_token_id = self.config.image_token_id
+        video_token_id = self.config.video_token_id
+        vision_start_token_id = self.config.vision_start_token_id
+        mrope_position_deltas = []
+        if image_grid_thw is not None or video_grid_thw is not None:
+            total_input_ids = input_ids
+            if attention_mask is None:
+                attention_mask = torch.ones_like(total_input_ids)
+            position_ids = torch.ones(
+                3, input_ids.shape[0], input_ids.shape[1], dtype=input_ids.dtype, device=input_ids.device
+            )
+            image_index, video_index = 0, 0
+            for i, input_ids in enumerate(total_input_ids):
+                input_ids = input_ids[attention_mask[i] == 1]
+                image_nums, video_nums = 0, 0
+                vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1)
+                vision_tokens = input_ids[vision_start_indices + 1]
+                image_nums = (vision_tokens == image_token_id).sum()
+                video_nums = (vision_tokens == video_token_id).sum()
+                input_tokens = input_ids.tolist()
+                llm_pos_ids_list: list = []
+                st = 0
+                remain_images, remain_videos = image_nums, video_nums
+                for _ in range(image_nums + video_nums):
+                    if image_token_id in input_tokens and remain_images > 0:
+                        ed_image = input_tokens.index(image_token_id, st)
+                    else:
+                        ed_image = len(input_tokens) + 1
+                    if video_token_id in input_tokens and remain_videos > 0:
+                        ed_video = input_tokens.index(video_token_id, st)
+                    else:
+                        ed_video = len(input_tokens) + 1
+                    if ed_image < ed_video:
+                        t, h, w = (
+                            image_grid_thw[image_index][0],
+                            image_grid_thw[image_index][1],
+                            image_grid_thw[image_index][2],
+                        )
+                        image_index += 1
+                        remain_images -= 1
+                        ed = ed_image
+                    else:
+                        t, h, w = (
+                            video_grid_thw[video_index][0],
+                            video_grid_thw[video_index][1],
+                            video_grid_thw[video_index][2],
+                        )
+                        video_index += 1
+                        remain_videos -= 1
+                        ed = ed_video
+                    llm_grid_t, llm_grid_h, llm_grid_w = (
+                        t.item(),
+                        h.item() // spatial_merge_size,
+                        w.item() // spatial_merge_size,
+                    )
+                    text_len = ed - st
+
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+                    llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+                    t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten()
+                    h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten()
+                    w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten()
+                    llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
+                    st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+                if st < len(input_tokens):
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+                    text_len = len(input_tokens) - st
+                    llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+                llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+                position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device)
+                mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i]))
+            mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1)
+            return position_ids, mrope_position_deltas
+        else:
+            if attention_mask is not None:
+                position_ids = attention_mask.long().cumsum(-1) - 1
+                position_ids.masked_fill_(attention_mask == 0, 1)
+                position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(input_ids.device)
+                max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0]
+                mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1]
+            else:
+                position_ids = (
+                    torch.arange(input_ids.shape[1], device=input_ids.device)
+                    .view(1, 1, -1)
+                    .expand(3, input_ids.shape[0], -1)
+                )
+                mrope_position_deltas = torch.zeros(
+                    [input_ids.shape[0], 1],
+                    device=input_ids.device,
+                    dtype=input_ids.dtype,
+                )
+
+            return position_ids, mrope_position_deltas
+
+    @add_start_docstrings_to_model_forward(Dimple_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=DimpleModelOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        pixel_values: Optional[torch.Tensor] = None,
+        pixel_values_videos: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = None,
+        rope_deltas: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        num_logits_to_keep: int = 0,
+        **loss_kwargs,
+    ) -> Union[Tuple, DimpleModelOutput]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, DimpleForConditionalGeneration
+
+        >>> model = DimpleForConditionalGeneration.from_pretrained(" ")
+        >>> processor = AutoProcessor.from_pretrained(" ")
+
+        >>> messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
+        ```"""
+        # logger.warning_once(
+        #     f"In {self.__class__}, Cache is {use_cache}, past_key_values is {past_key_values}, cache position is {cache_position}."
+        # )
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if inputs_embeds is None:
+            inputs_embeds = self.model.embed_tokens(input_ids)
+            if pixel_values is not None:
+                pixel_values = pixel_values.type(self.visual.get_dtype())
+                image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+                n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
+                n_image_features = image_embeds.shape[0]
+                if n_image_tokens != n_image_features:
+                    raise ValueError(
+                        f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+                    )
+                image_mask = (
+                    (input_ids == self.config.image_token_id)
+                    .unsqueeze(-1)
+                    .expand_as(inputs_embeds)
+                    .to(inputs_embeds.device)
+                )
+                image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+                image_embeds_projected = self.img_projector(image_embeds)
+                inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds_projected)
+
+            if pixel_values_videos is not None:
+                pixel_values_videos = pixel_values_videos.type(self.visual.get_dtype())
+                video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
+                n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
+                n_video_features = video_embeds.shape[0]
+                if n_video_tokens != n_video_features:
+                    raise ValueError(
+                        f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
+                    )
+                video_mask = (
+                    (input_ids == self.config.video_token_id)
+                    .unsqueeze(-1)
+                    .expand_as(inputs_embeds)
+                    .to(inputs_embeds.device)
+                )
+                video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+                raise NotImplementedError(
+                    "Video feature projector not implemented"
+                )
+                inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
+
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(inputs_embeds.device)
+
+        outputs = self.model(
+            input_ids=None, # in Qwen
+            # input_ids=input_ids,# in Dream
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        # in Qwen
+        logits = self.lm_head(hidden_states)
+        # in Dream
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        # logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
+
+
+        loss = None
+        if labels is not None:
+            logger.warning_once(
+                "Use the naive next token prediction loss for the model. This is not the same as the original Dimple model, which uses a different loss function."
+            )
+            # in Qwen
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+            # in Dream
+            # loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return DimpleModelOutput(
+            logits=logits,
+            loss=loss,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            rope_deltas=rope_deltas,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        pixel_values=None,
+        pixel_values_videos=None,
+        image_grid_thw=None,
+        video_grid_thw=None,
+        rope_deltas = None,
+        **kwargs,
+    ):
+        # never remove input ids
+
+        if use_cache:
+            if past_key_values is None:
+                raise ValueError(
+                    "If `use_cache=True`, `past_key_values` must be provided. Please make sure to pass `past_key_values` to the model."
+                )
+            else:
+                pass
+        else:
+            past_key_values = None
+
+        if use_cache:
+            if cache_position is None:
+                raise ValueError(
+                    "If `use_cache=True`, `cache_position` must be provided. Please make sure to pass `cache_position` to the model."
+                )
+            else:
+                pass
+        else:
+            cache_position = None
+
+        if use_cache:
+            if input_ids.shape[1] != cache_position.shape[0]:
+                input_ids = input_ids[:, cache_position]
+            else:
+                pass
+        else:
+            pass
+
+        if position_ids is None:
+            if not use_cache:
+                position_ids, rope_deltas = self.get_rope_index(
+                    input_ids, image_grid_thw, video_grid_thw, attention_mask
+                )
+            else:
+                if cache_position[0] == 0:
+                    position_ids, rope_deltas = self.get_rope_index(
+                        input_ids, image_grid_thw, video_grid_thw, attention_mask
+                    )
+                else:
+                    batch_size, seq_length = input_ids.shape
+                    delta = (
+                        cache_position[0] + rope_deltas if cache_position is not None and rope_deltas is not None else 0
+                    )
+                    position_ids = torch.arange(seq_length, device=input_ids.device)
+                    position_ids = position_ids.view(1, -1).expand(batch_size, -1)
+                    position_ids = position_ids.add(delta)
+                    position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
+    
+        else:
+            raise NotImplementedError(
+                "position_ids is not None, please check the code in prepare_inputs_for_generation"
+            )
+
+        if use_cache:
+            if cache_position[0] != 0:
+                pixel_values = None
+                pixel_values_videos = None
+                logger.debug(f"after prefill, the pixel_values and pixel_values_videos are None.")
+        else:
+            pass
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None:
+            raise NotImplementedError(
+                "inputs_embeds is not None, please check the code in prepare_inputs_for_generation"
+            )
+        else:
+            model_inputs = {"input_ids": input_ids, "inputs_embeds": None}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+                "pixel_values": pixel_values,
+                "pixel_values_videos": pixel_values_videos,
+                "image_grid_thw": image_grid_thw,
+                "video_grid_thw": video_grid_thw,
+                "cache_position": cache_position,
+                "rope_deltas": rope_deltas,
+            }
+        )
+        return model_inputs