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modeling_emova.py

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+# coding=utf-8
+# Copyright 2024 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.
+"""PyTorch EMOVA model."""
+
+import math
+from dataclasses import dataclass
+from functools import partial
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from transformers import PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.image_processing_utils import select_best_resolution
+from transformers.modeling_outputs import ModelOutput
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.models.auto import AutoModel, AutoModelForCausalLM
+
+from .configuration_emova import EMOVAConfig
+from .modeling_qwen2vit import Qwen2VisionTower
+
+from timm.models.regnet import RegStage
+
+try:
+    from timm.layers import LayerNorm2d
+except:
+    from timm.models.layers import LayerNorm2d
+from einops import rearrange
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "EMOVAConfig"
+
+
+@dataclass
+class EMOVACausalLMOutputWithPast(ModelOutput):
+    """
+    Base class for EMOVA causal language model (or autoregressive) 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.
+        image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
+            sequence_length, hidden_size)`.
+
+            image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+
+
+class EMOVAMultiModalProjector(nn.Sequential):
+    # CAbstractor
+    def __init__(self, config):
+        super(EMOVAMultiModalProjector, self).__init__()
+        hidden_size = config.text_config.hidden_size
+        mm_hidden_size = config.vision_config.hidden_size
+        mlp_depth = config.mm_projector_config['mlp_depth']
+
+        modules = [nn.Linear(mm_hidden_size, hidden_size)]
+        for _ in range(1, mlp_depth):
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(hidden_size, hidden_size))
+        super(EMOVAMultiModalProjector, self).__init__(*modules)
+
+
+EMOVA_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 ([`EMOVAConfig`] or [`EMOVAVisionConfig`]):
+            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 LLaMA Model outputting raw hidden-states without any specific head on top.",
+    EMOVA_START_DOCSTRING,
+)
+class EMOVAPreTrainedModel(PreTrainedModel):
+    config_class = EMOVAConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["EMOVAVisionAttention"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = (
+            self.config.initializer_range
+            if hasattr(self.config, "initializer_range")
+            else self.config.text_config.initializer_range
+        )
+
+        if hasattr(module, "class_embedding"):
+            module.class_embedding.data.normal_(mean=0.0, std=std)
+
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            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_()
+
+    @property
+    def _supports_sdpa(self):
+        """
+        Retrieve language_model's attribute to check whether the model supports
+        SDPA or not.
+        """
+        return self.language_model._supports_sdpa
+
+
+EMOVA_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)
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
+            The tensors corresponding to the input images. Pixel values can be obtained using
+            [`AutoImageProcessor`]. See [`EMOVAImageProcessor.__call__`] for details. [`EMOVAProcessor`] uses
+            [`EMOVAImageProcessor`] for processing images.
+        image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, *optional*):
+            The sizes of the images in the batch, being (height, width) for each image.
+        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.
+        vision_feature_layer (`int`, *optional*, defaults to -2):
+            The index of the layer to select the vision feature.
+        vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+            The feature selection strategy used to select the vision feature from the vision backbone.
+            Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
+            If `"full"`, the full vision features are used.
+        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.
+"""
+
+
+@add_start_docstrings(
+    """The EMOVA model which consists of a vision backbone and a language model.""",
+    EMOVA_START_DOCSTRING,
+)
+class EMOVAForConditionalGeneration(EMOVAPreTrainedModel):
+    def __init__(self, config: EMOVAConfig, **kwargs):
+        super().__init__(config)
+        self.vision_tower = Qwen2VisionTower(config.vision_config)
+        self.multi_modal_projector = EMOVAMultiModalProjector(config)
+
+        self.vocab_size = config.text_config.vocab_size
+        self.language_model = AutoModelForCausalLM.from_config(
+            config.text_config, attn_implementation=config._attn_implementation
+        )
+        self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+        self._padding_side = "left"  # set it to left by default, user can use setter to change padding_sides
+        self.post_init()
+
+    @property
+    def padding_side(self):
+        return self._padding_side
+
+    @padding_side.setter
+    def padding_side(self, padding_side: str):
+        if padding_side not in ["left", "right"]:
+            raise ValueError(f"{padding_side} is not `left` or `right`.")
+        self._padding_side = padding_side
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.language_model.get_output_embeddings()
+
+    def set_output_embeddings(self, new_embeddings):
+        self.language_model.set_output_embeddings(new_embeddings)
+
+    def set_decoder(self, decoder):
+        self.language_model.set_decoder(decoder)
+
+    def get_decoder(self):
+        return self.language_model.get_decoder()
+
+    def tie_weights(self):
+        return self.language_model.tie_weights()
+
+    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
+        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+        # update vocab size
+        self.config.text_config.vocab_size = model_embeds.num_embeddings
+        self.vocab_size = model_embeds.num_embeddings
+        return model_embeds
+
+    def _merge_input_ids_with_image_features(
+            self,
+            image_features,
+            feature_lens,
+            inputs_embeds,
+            input_ids,
+            attention_mask,
+            position_ids=None,
+            labels=None,
+            image_token_index=None,
+            ignore_index=-100,
+    ):
+        """
+        Merge input_ids with with image features into final embeddings
+
+        Args:
+            image_features (`torch.Tensor` of shape `(all_feature_lens, embed_dim)`):
+                All vision vectors of all images in the batch
+            feature_lens (`torch.LongTensor` of shape `(num_images)`):
+                The length of visual embeddings of each image as stacked in `image_features`
+            inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, embed_dim)`):
+                Token embeddings before merging with visual embeddings
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Input_ids of tokens, possibly filled with image token
+            attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Mask to avoid performing attention on padding token indices.
+            position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+                config.n_positions - 1]`.
+            labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*)
+                :abels need to be recalculated to support training (if provided)
+            image_token_index (`int`, *optional*)
+                Token id used to indicate the special "image" token. Defaults to `config.image_token_index`
+            ignore_index (`int`, *optional*)
+                Value that is used to pad `labels` and will be ignored when calculated loss. Default: -100.
+        Returns:
+            final_embedding, final_attention_mask, position_ids, final_labels
+
+        Explanation:
+            each image has variable length embeddings, with length specified by feature_lens
+            image_features is concatenation of all visual embed vectors
+            task: fill each <image> with the correct number of visual embeddings
+            Example:
+                X (5 patches), Y (3 patches), Z (8)
+                X, Y are in the same sequence (in-context learning)
+            if right padding
+                input_ids: [
+                    a b c d e f X g h i j k Y l m
+                    o p q r Z s t u v _ _ _ _ _ _
+                ]
+                input_ids should be: [
+                    a b c d e f X X X X X g h i j k Y Y Y l m
+                    o p q r Z Z Z Z Z Z Z Z s t u v _ _ _ _ _
+                ]
+                labels should be: [
+                    a b c d e f _ _ _ _ _ g h i j k _ _ _ l m
+                    o p q r _ _ _ _ _ _ _ _ s t u v _ _ _ _ _
+                ]
+            elif left padding
+                input_ids: [
+                    a b c d e f X g h i j k Y l m
+                    _ _ _ _ _ _ o p q r Z s t u v
+                ]
+                input_ids should be: [
+                    a b c d e f X X X X X g h i j k Y Y Y l m
+                    _ _ _ _ _ o p q r Z Z Z Z Z Z Z Z s t u v
+                ]
+                labels should be: [
+                    a b c d e f _ _ _ _ _ g h i j k _ _ _ l m
+                    _ _ _ _ _ o p q r _ _ _ _ _ _ _ _ s t u v
+                ]
+            Edge cases:
+                * If tokens are same but image token sizes are different, then cannot infer left or right padding
+                ```python
+                cat_img = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+                chart_img = Image.open(requests.get("https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true", stream=True).raw)
+                prompts = [
+                    "[INST] <image>\nWhat is shown in this image? [/INST]",
+                    "[INST] <image>\nWhat is shown in this image? [/INST]",
+                ]
+                inputs = processor(prompts, [chart_img, cat_img], return_tensors='pt', padding=True).to("cuda")
+                    chart_img has 2634 tokens, while cat_img has 2340 tokens
+                ```
+
+                input_ids: [
+                    a b c d X g h
+                    i j Y k l m n
+                ]
+                where X is 3 tokens while Y is 5, this mean after merge
+                if left-padding (batched generation)
+                    input_ids should be: [
+                        _ _ a b c d X X X g h
+                        i j Y Y Y Y Y k l m n
+                    ]
+                elif (right padding) (training)
+                    input_ids should be: [
+                        a b c d X X X g h _ _
+                        i j Y Y Y Y Y k l m n
+                    ]
+        """
+        image_token_index = image_token_index if image_token_index is not None else self.config.image_token_index
+        ignore_index = ignore_index if ignore_index is not None else self.config.ignore_index
+
+        with torch.no_grad():
+            num_images = feature_lens.size(0)
+            num_image_features, embed_dim = image_features.shape
+            if feature_lens.sum() != num_image_features:
+                raise ValueError(f"{feature_lens=} / {feature_lens.sum()} != {image_features.shape=}")
+            batch_size = input_ids.shape[0]
+            _left_padding = torch.any(attention_mask[:, 0] == 0)
+            _right_padding = torch.any(attention_mask[:, -1] == 0)
+
+            left_padding = True if not self.training else False
+            if batch_size > 1 and not self.training:
+                if _left_padding and not _right_padding:
+                    left_padding = True
+                elif not _left_padding and _right_padding:
+                    left_padding = False
+                elif not _left_padding and not _right_padding:
+                    # both side is 1, so cannot tell
+                    left_padding = self.padding_side == "left"
+                else:
+                    # invalid attention_mask
+                    raise ValueError(f"both side of attention_mask has zero, invalid. {attention_mask}")
+
+            # Whether to turn off right padding
+            # 1. Create a mask to know where special image tokens are
+            special_image_token_mask = input_ids == image_token_index
+            # special_image_token_mask: [bsz, seqlen]
+            num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
+            # num_special_image_tokens: [bsz]
+            # Reserve for padding of num_images
+            total_num_special_image_tokens = torch.sum(special_image_token_mask)
+            if total_num_special_image_tokens != num_images:
+                raise ValueError(
+                    f"Number of image tokens in input_ids ({total_num_special_image_tokens}) different from num_images ({num_images})."
+                )
+            # Compute the maximum embed dimension
+            # max_image_feature_lens is max_feature_lens per batch
+            feature_lens = feature_lens.to(input_ids.device)
+            feature_lens_batch = feature_lens.split(num_special_image_tokens.tolist(), dim=0)
+            feature_lens_batch_sum = torch.tensor([x.sum() for x in feature_lens_batch], device=input_ids.device)
+            embed_sequence_lengths = (
+                    (attention_mask == 1).long().sum(-1) - num_special_image_tokens + feature_lens_batch_sum
+            )
+            max_embed_dim = embed_sequence_lengths.max()
+
+            batch_indices, non_image_indices = torch.where((input_ids != image_token_index) & (attention_mask == 1))
+            # 2. Compute the positions where text should be written
+            # Calculate new positions for text tokens in merged image-text sequence.
+            # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images` text tokens.
+            # `torch.cumsum` computes how each image token shifts subsequent text token positions.
+            # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
+            # ! instead of special_image_token_mask * (num_image_patches - 1)
+            #   special_image_token_mask * (num_feature_len - 1)
+            special_image_token_mask = special_image_token_mask.long()
+            special_image_token_mask[special_image_token_mask == 1] = feature_lens - 1
+            new_token_positions = torch.cumsum((special_image_token_mask + 1), -1) - 1
+            if left_padding:
+                # shift right token positions so that they are ending at the same number
+                # the below here was incorrect? new_token_positions += new_token_positions[:, -1].max() - new_token_positions[:, -1:]
+                new_token_positions += max_embed_dim - 1 - new_token_positions[:, -1:]
+
+            text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
+
+        # 3. Create the full embedding, already padded to the maximum position
+        final_embedding = torch.zeros(
+            batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+        )
+        final_attention_mask = torch.zeros(
+            batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
+        )
+        final_input_ids = torch.full(
+            (batch_size, max_embed_dim), self.pad_token_id, dtype=input_ids.dtype, device=inputs_embeds.device
+        )
+        # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
+        # set the corresponding tensors into their correct target device.
+        target_device = inputs_embeds.device
+        batch_indices, non_image_indices, text_to_overwrite = (
+            batch_indices.to(target_device),
+            non_image_indices.to(target_device),
+            text_to_overwrite.to(target_device),
+        )
+        attention_mask = attention_mask.to(target_device)
+        input_ids = input_ids.to(target_device)
+
+        # 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
+        # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
+        final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
+        final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
+        final_input_ids[batch_indices, text_to_overwrite] = input_ids[batch_indices, non_image_indices]
+        final_labels = None
+        if labels is not None:
+            labels = labels.to(target_device)
+            final_labels = torch.full_like(final_attention_mask, ignore_index).to(torch.long)
+            final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
+
+        # 5. Fill the embeddings corresponding to the images. Anything that is not `text_positions` needs filling (#29835)
+        with torch.no_grad():
+            image_to_overwrite = torch.full(
+                (batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device
+            )
+            image_to_overwrite[batch_indices, text_to_overwrite] = False
+            embed_indices = torch.arange(max_embed_dim).unsqueeze(0).to(target_device)
+            embed_indices = embed_indices.expand(batch_size, max_embed_dim)
+            embed_seq_lens = embed_sequence_lengths[:, None].to(target_device)
+
+            if left_padding:
+                # exclude padding on the left
+                max_embed_dim = max_embed_dim.to(target_device)
+                val = (max_embed_dim - embed_indices) <= embed_seq_lens
+            else:
+                # exclude padding on the right
+                val = embed_indices < embed_seq_lens
+            image_to_overwrite &= val
+
+            if image_to_overwrite.sum() != num_image_features:
+                raise ValueError(
+                    f"{image_to_overwrite.sum()=} != {num_image_features=} The input provided to the model are wrong. "
+                    f"The number of image tokens is {torch.sum(special_image_token_mask)} while"
+                    f" the number of image given to the model is {num_images}. "
+                    f"This prevents correct indexing and breaks batch generation."
+                )
+        final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
+        final_attention_mask |= image_to_overwrite
+        position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
+
+        return final_embedding, final_attention_mask, position_ids, final_labels, final_input_ids
+
+    @add_start_docstrings_to_model_forward(EMOVA_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=EMOVACausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            pixel_values: torch.FloatTensor = None,
+            image_sizes: Optional[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,
+            vision_feature_layer: Optional[int] = None,
+            vision_feature_select_strategy: Optional[str] = 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,
+    ) -> Union[Tuple, EMOVACausalLMOutputWithPast]:
+        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, EMOVAForConditionalGeneration
+
+        >>> model = EMOVAForConditionalGeneration.from_pretrained("Emova-ollm/emova-qwen-2-5-7b-hf")
+        >>> processor = AutoProcessor.from_pretrained("Emova-ollm/emova-qwen-2-5-7b-hf")
+
+        >>> prompt = "<image>\nWhat is shown in this image?"
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = processor(text=prompt, images=image, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(**inputs, max_length=30)
+        >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "\nWhat is shown in this image? The image appears to be a radar chart, which is a type of multi-dimensional plot (...)"
+        ```"""
+
+        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:
+            # 1. Extract the input embeddings
+            # In case image_token_index is not in the embeddings (extra token but embedding don't have it)
+            for_inputs_embeds_ids = input_ids.clone()
+            for_inputs_embeds_ids[(input_ids == self.config.image_token_index)] = 0
+            inputs_embeds = self.get_input_embeddings()(for_inputs_embeds_ids)
+
+            # 2. Merge text and images
+            if pixel_values is not None and input_ids.shape[1] != 1 and pixel_values.size(0) > 0:
+                # ! infer image_num_patches from image_sizes
+
+                image_features = self.vision_tower(pixel_values.to(self.dtype), image_sizes)
+                image_features = self.multi_modal_projector(image_features)
+
+                spatial_merge_size = self.vision_tower.spatial_merge_size
+                feature_lens = torch.as_tensor(
+                    [t * h * w // (self.vision_tower.spatial_merge_size ** 2) for t, h, w in image_sizes])
+                image_num_patches = sum(feature_lens)
+
+                # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
+                inputs_embeds = inputs_embeds.to(image_features.dtype)
+                inputs_embeds, attention_mask, position_ids, labels, _ = self._merge_input_ids_with_image_features(
+                    image_features,
+                    feature_lens,
+                    inputs_embeds,
+                    input_ids,
+                    attention_mask,
+                    position_ids,
+                    labels=labels,
+                )
+
+            # pixel_values is not None but is empty ---> text only cases
+            elif pixel_values is not None and input_ids.shape[1] != 1 and pixel_values.size(0) == 0:
+                # there are no images
+                pass
+
+            # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
+            # generation with cache
+            elif past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1:
+                # Retrieve the first layer to inspect the logits and mask out the hidden states
+                # that are set to 0
+                first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
+
+                # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
+                batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
+
+                # Get the target length
+                target_length = input_ids.shape[1]
+                past_length = first_layer_past_key_value.shape[-1]
+
+                extended_attention_mask = torch.ones(
+                    (attention_mask.shape[0], past_length),
+                    dtype=attention_mask.dtype,
+                    device=attention_mask.device,
+                )
+
+                # Filter out only the tokens that can be un-attended, this can happen
+                # if one uses EMOVA + Fused modules where the cache on the
+                # first iteration is already big enough, or if one passes custom cache
+                valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
+                new_batch_index = batch_index[valid_indices]
+                new_non_attended_tokens = non_attended_tokens[valid_indices]
+
+                # Zero-out the places where we don't need to attend
+                extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
+
+                attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
+
+                position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
+
+        outputs = self.language_model(
+            attention_mask=attention_mask.to(inputs_embeds.device),
+            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,
+        )
+
+        logits = outputs[0]
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            if attention_mask is not None:
+                shift_attention_mask = attention_mask[..., 1:]
+                shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
+                shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
+            else:
+                shift_logits = logits[..., :-1, :].contiguous()
+                shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
+            )
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return EMOVACausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+            self,
+            input_ids,
+            past_key_values=None,
+            inputs_embeds=None,
+            pixel_values=None,
+            image_sizes=None,
+            attention_mask=None,
+            **kwargs,
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+            elif self.config.image_token_index in input_ids:
+                input_ids = input_ids[:, input_ids.shape[1] - 1:]
+            # If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
+            # older attention values, as their corresponding values are not part of the input.
+            if cache_length < past_length and attention_mask is not None:
+                attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]):]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1]:]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+                "pixel_values": pixel_values,
+                "image_sizes": image_sizes,
+            }
+        )
+        return model_inputs
+
+    def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
+             num_patches_list=None, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>',
+             verbose=False):
+        raise RuntimeError("!!!")
+
+        if history is None and pixel_values is not None and '<image>' not in question:
+            question = '<image>\n' + question
+
+        if num_patches_list is None:
+            num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
+        assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
+
+        img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
+        self.img_context_token_id = img_context_token_id
+
+        template = get_conv_template(self.template)
+        template.system_message = self.system_message
+        eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
+
+        history = [] if history is None else history
+        for (old_question, old_answer) in history:
+            template.append_message(template.roles[0], old_question)
+            template.append_message(template.roles[1], old_answer)
+        template.append_message(template.roles[0], question)
+        template.append_message(template.roles[1], None)
+        query = template.get_prompt()
+
+        if verbose and pixel_values is not None:
+            image_bs = pixel_values.shape[0]
+            print(f'dynamic ViT batch size: {image_bs}')
+
+        for num_patches in num_patches_list:
+            image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
+            query = query.replace('<image>', image_tokens, 1)
+
+        model_inputs = tokenizer(query, return_tensors='pt')
+        input_ids = model_inputs['input_ids'].cuda()
+        attention_mask = model_inputs['attention_mask'].cuda()
+        generation_config['eos_token_id'] = eos_token_id
+        generation_output = self.generate(
+            pixel_values=pixel_values,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            **generation_config
+        )
+        response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
+        response = response.split(template.sep)[0].strip()
+        history.append((question, response))
+        if return_history:
+            return response, history
+        else:
+            query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
+            query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
+            if verbose:
+                print(query_to_print, response)
+            return response
+
+    def _reorder_cache(self, *args, **kwargs):
+        return self.language_model._reorder_cache(*args, **kwargs)

modeling_qwen2vit.py

@@ -0,0 +1,335 @@
+# coding=utf-8
+# Copyright 2024 The Qwen team, Alibaba Group 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 Qwen2-VL model."""
+
+import math
+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.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    is_flash_attn_2_available,
+    logging,
+)
+from .configuration_qwen2vit import Qwen2VLVisionConfig
+
+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(__name__)
+
+
+# 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_rotary_pos_emb_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
+    orig_dtype = tensor.dtype
+    tensor = tensor.float()
+    cos = freqs.cos()
+    sin = freqs.sin()
+    cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
+    sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
+    output = (tensor * cos) + (rotate_half(tensor) * sin)
+    output = output.to(orig_dtype)
+    return output
+
+
+class VisionRotaryEmbedding(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 PatchEmbed(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 PatchMerger(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 = LayerNorm(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 VisionMlp(nn.Module):
+    def __init__(self, dim: int, hidden_dim: int, hidden_act: str) -> None:
+        super().__init__()
+        self.fc1 = nn.Linear(dim, hidden_dim)
+        self.act = ACT2FN[hidden_act]
+        self.fc2 = nn.Linear(hidden_dim, dim)
+
+    def forward(self, x) -> torch.Tensor:
+        return self.fc2(self.act(self.fc1(x)))
+
+
+class VisionAttention(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: 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)
+        q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
+        k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
+
+        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 VisionFlashAttention2(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: 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)
+        q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
+        k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
+
+        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
+        attn_output = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape(
+            seq_length, -1
+        )
+        attn_output = self.proj(attn_output)
+        return attn_output
+
+
+class VisionSdpaAttention(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: 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)
+        q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
+        k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
+
+        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, k, v, attention_mask, dropout_p=0.0)
+        attn_output = attn_output.transpose(0, 1)
+        attn_output = attn_output.reshape(seq_length, -1)
+        attn_output = self.proj(attn_output)
+        return attn_output
+
+
+QWEN2_VL_VISION_ATTENTION_CLASSES = {
+    "eager": VisionAttention,
+    "flash_attention_2": VisionFlashAttention2,
+    "sdpa": VisionSdpaAttention,
+}
+
+
+class Qwen2VLVisionBlock(nn.Module):
+    def __init__(self, config, attn_implementation: str = "sdpa") -> None:
+        super().__init__()
+        self.norm1 = LayerNorm(config.embed_dim, eps=1e-6)
+        self.norm2 = LayerNorm(config.embed_dim, eps=1e-6)
+        mlp_hidden_dim = int(config.embed_dim * config.mlp_ratio)
+
+        self.attn = QWEN2_VL_VISION_ATTENTION_CLASSES[attn_implementation](
+            config.embed_dim, num_heads=config.num_heads
+        )
+        self.mlp = VisionMlp(dim=config.embed_dim, hidden_dim=mlp_hidden_dim, hidden_act=config.hidden_act)
+
+    def forward(self, hidden_states, cu_seqlens, rotary_pos_emb) -> torch.Tensor:
+        hidden_states = hidden_states + self.attn(
+            self.norm1(hidden_states), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
+        )
+        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+        return hidden_states
+
+
+class Qwen2VisionTower(PreTrainedModel):
+    config_class = Qwen2VLVisionConfig
+    _no_split_modules = ["Qwen2VLVisionBlock"]
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _supports_flash_attn_2 = True
+    _supports_sdpa = 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_()
+
+    def __init__(self, config) -> None:
+        super().__init__(config)
+        self.spatial_merge_size = config.spatial_merge_size
+
+        self.patch_embed = PatchEmbed(
+            patch_size=config.patch_size,
+            temporal_patch_size=config.temporal_patch_size,
+            in_channels=config.in_channels,
+            embed_dim=config.embed_dim,
+        )
+
+        head_dim = config.embed_dim // config.num_heads
+        self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList(
+            [Qwen2VLVisionBlock(config, "eager") for _ in range(config.depth)]
+        )
+        self.merger = PatchMerger(
+            dim=config.hidden_size, context_dim=config.embed_dim, spatial_merge_size=config.spatial_merge_size
+        )
+
+    def get_dtype(self) -> torch.dtype:
+        return self.blocks[0].mlp.fc2.weight.dtype
+
+    def get_device(self) -> torch.device:
+        return self.blocks[0].mlp.fc2.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 forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.patch_embed(hidden_states)
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
+            dim=0, dtype=torch.int32
+        )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        for blk in self.blocks:
+            hidden_states = blk(hidden_states, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb)
+
+        return self.merger(hidden_states)

modeling_rope_utils.py

@@ -0,0 +1,558 @@
+# Copyright 2024 The HuggingFace 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 math
+from typing import Optional, Tuple
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import is_torch_available, logging
+
+logger = logging.get_logger(__name__)
+
+if is_torch_available():
+    import torch
+
+
+def _compute_default_rope_parameters(
+        config: Optional[PretrainedConfig] = None,
+        device: Optional["torch.device"] = None,
+        seq_len: Optional[int] = None,
+        **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies according to the original RoPE implementation
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_default_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        base = rope_kwargs["base"]
+        dim = rope_kwargs["dim"]
+    elif config is not None:
+        base = config.rope_theta
+        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        dim = int(head_dim * partial_rotary_factor)
+
+    attention_factor = 1.0  # Unused in this type of RoPE
+
+    # Compute the inverse frequencies
+    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
+    return inv_freq, attention_factor
+
+
+def _compute_linear_scaling_rope_parameters(
+        config: Optional[PretrainedConfig] = None,
+        device: Optional["torch.device"] = None,
+        seq_len: Optional[int] = None,
+        **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with linear scaling. Credits to the Reddit user /u/kaiokendev
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_linear_scaling_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        factor = rope_kwargs["factor"]
+    elif config is not None:
+        factor = config.rope_scaling["factor"]
+
+    # Gets the default RoPE parameters
+    inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
+
+    # Then applies linear scaling to the frequencies.
+    # NOTE: originally, scaling was applied to the position_ids. However, we get `embs = inv_freq @ position_ids`, so
+    # applying scaling to the inverse frequencies is equivalent.
+    inv_freq /= factor
+    return inv_freq, attention_factor
+
+
+def _compute_dynamic_ntk_parameters(
+        config: Optional[PretrainedConfig] = None,
+        device: Optional["torch.device"] = None,
+        seq_len: Optional[int] = None,
+        **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length, used to update the dynamic RoPE at inference time.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_dynamic_ntk_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        base = rope_kwargs["base"]
+        dim = rope_kwargs["dim"]
+        max_position_embeddings = rope_kwargs["max_position_embeddings"]
+        factor = rope_kwargs["factor"]
+    elif config is not None:
+        base = config.rope_theta
+        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        dim = int(head_dim * partial_rotary_factor)
+        max_position_embeddings = config.max_position_embeddings
+        factor = config.rope_scaling["factor"]
+
+    attention_factor = 1.0  # Unused in this type of RoPE
+
+    # seq_len: default to max_position_embeddings, e.g. at init time
+    seq_len = seq_len if seq_len is not None and seq_len > max_position_embeddings else max_position_embeddings
+
+    # Compute the inverse frequencies
+    base = base * ((factor * seq_len / max_position_embeddings) - (factor - 1)) ** (dim / (dim - 2))
+    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
+    return inv_freq, attention_factor
+
+
+def _compute_yarn_parameters(
+        config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with NTK scaling. Please refer to the
+    [original paper](https://arxiv.org/abs/2309.00071)
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # No need to keep BC with yarn, unreleased when this new pattern was created.
+    if len(rope_kwargs) > 0:
+        raise ValueError(
+            f"Unexpected arguments: `**rope_kwargs` should be unset in `_compute_yarn_parameters`, got {rope_kwargs}"
+        )
+
+    base = config.rope_theta
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
+    max_position_embeddings = config.max_position_embeddings
+    factor = config.rope_scaling["factor"]
+
+    # Sets the attention factor as suggested in the paper
+    attention_factor = config.rope_scaling.get("attention_factor")
+    if attention_factor is None:
+        attention_factor = 0.1 * math.log(factor) + 1.0
+
+    # Optional config options
+    # beta_fast/beta_slow: as suggested in the paper, default to 32/1 (correspondingly)
+    beta_fast = config.rope_scaling.get("beta_fast") or 32
+    beta_slow = config.rope_scaling.get("beta_slow") or 1
+
+    # Compute the inverse frequencies
+    def find_correction_dim(num_rotations, dim, base, max_position_embeddings):
+        """Inverse dimension formula to find the dimension based on the number of rotations"""
+        return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))
+
+    def find_correction_range(low_rot, high_rot, dim, base, max_position_embeddings):
+        """Find dimension range bounds based on rotations"""
+        low = math.floor(find_correction_dim(low_rot, dim, base, max_position_embeddings))
+        high = math.ceil(find_correction_dim(high_rot, dim, base, max_position_embeddings))
+        return max(low, 0), min(high, dim - 1)
+
+    def linear_ramp_factor(min, max, dim):
+        if min == max:
+            max += 0.001  # Prevent singularity
+
+        linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+        ramp_func = torch.clamp(linear_func, 0, 1)
+        return ramp_func
+
+    # Note on variable naming: "interpolation" comes from the original technique, where we interpolate the position IDs
+    # to expand the possible context length. In other words, interpolation = apply scaling factor.
+    pos_freqs = base ** (torch.arange(0, dim, 2).float().to(device) / dim)
+    inv_freq_extrapolation = 1.0 / pos_freqs
+    inv_freq_interpolation = 1.0 / (factor * pos_freqs)
+
+    low, high = find_correction_range(beta_fast, beta_slow, dim, base, max_position_embeddings)
+
+    # Get n-dimensional rotational scaling corrected for extrapolation
+    inv_freq_extrapolation_factor = 1 - linear_ramp_factor(low, high, dim // 2).float().to(device)
+    inv_freq = (
+            inv_freq_interpolation * (1 - inv_freq_extrapolation_factor)
+            + inv_freq_extrapolation * inv_freq_extrapolation_factor
+    )
+
+    return inv_freq, attention_factor
+
+
+def _compute_longrope_parameters(
+        config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with LongRoPE scaling. Please refer to the
+    [original implementation](https://github.com/microsoft/LongRoPE)
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
+    # No need to keep BC with longrope, unreleased when this new pattern was created.
+    if len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` should be unset in `_compute_longrope_parameters`, got "
+            f"{rope_kwargs}"
+        )
+
+    base = config.rope_theta
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
+    long_factor = config.rope_scaling["long_factor"]
+    short_factor = config.rope_scaling["short_factor"]
+    factor = config.rope_scaling.get("factor")
+    attention_factor = config.rope_scaling.get("attention_factor")
+
+    # NOTE: Phi3 (and potentially other models) modify `max_position_embeddings` and have a
+    # `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
+    # values to compute the default attention scaling factor, instead of using `factor`.
+    if hasattr(config, "original_max_position_embeddings"):
+        max_position_embeddings = config.original_max_position_embeddings
+        expanded_max_position_embeddings = config.max_position_embeddings
+        factor = expanded_max_position_embeddings / max_position_embeddings
+    else:
+        max_position_embeddings = config.max_position_embeddings
+        expanded_max_position_embeddings = max_position_embeddings * factor
+
+    # Sets the attention factor as suggested in the paper
+    if attention_factor is None:
+        if factor <= 1.0:
+            attention_factor = 1.0
+        else:
+            attention_factor = math.sqrt(1 + math.log(factor) / math.log(max_position_embeddings))
+
+    # Compute the inverse frequencies -- scaled based on the target sequence length
+    if expanded_max_position_embeddings > max_position_embeddings:
+        ext_factors = torch.tensor(long_factor, dtype=torch.float32, device=device)
+    else:
+        ext_factors = torch.tensor(short_factor, dtype=torch.float32, device=device)
+    inv_freq_shape = torch.arange(0, dim, 2, dtype=torch.int64, device=device).float() / dim
+    inv_freq = 1.0 / (ext_factors * base ** inv_freq_shape)
+
+    return inv_freq, attention_factor
+
+
+def _compute_llama3_parameters(
+        config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies for llama 3.1.
+
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # Gets the default RoPE parameters
+    inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
+
+    factor = config.rope_scaling["factor"]  # `8` in the original implementation
+    low_freq_factor = config.rope_scaling["low_freq_factor"]  # `1` in the original implementation
+    high_freq_factor = config.rope_scaling["high_freq_factor"]  # `4` in the original implementation
+    old_context_len = config.rope_scaling["original_max_position_embeddings"]  # `8192` in the original implementation
+
+    low_freq_wavelen = old_context_len / low_freq_factor
+    high_freq_wavelen = old_context_len / high_freq_factor
+
+    wavelen = 2 * math.pi / inv_freq
+    # wavelen < high_freq_wavelen: do nothing
+    # wavelen > low_freq_wavelen: divide by factor
+    inv_freq_llama = torch.where(wavelen > low_freq_wavelen, inv_freq / factor, inv_freq)
+    # otherwise: interpolate between the two, using a smooth factor
+    smooth_factor = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+    smoothed_inv_freq = (1 - smooth_factor) * inv_freq_llama / factor + smooth_factor * inv_freq_llama
+    is_medium_freq = ~(wavelen < high_freq_wavelen) * ~(wavelen > low_freq_wavelen)
+    inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
+
+    return inv_freq_llama, attention_factor
+
+
+# This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters
+# from the model config. You can append new {'rope_type': callable} pairs to this dictionary to enable custom RoPE
+# parameterizations, as long as the callable has the same signature.
+ROPE_INIT_FUNCTIONS = {
+    "default": _compute_default_rope_parameters,
+    "linear": _compute_linear_scaling_rope_parameters,
+    "dynamic": _compute_dynamic_ntk_parameters,
+    "yarn": _compute_yarn_parameters,
+    "longrope": _compute_longrope_parameters,
+    "llama3": _compute_llama3_parameters,
+}
+
+
+def _check_received_keys(rope_type: str, received_keys: set, required_keys: set, optional_keys: Optional[set] = None):
+    """Compare the received keys in `config.rope_scaling` against the expected and optional keys"""
+    # BC: "rope_type" was originally "type" -- let's check for "rope_type" when "type" is present
+    if "type" in received_keys:
+        received_keys -= {"type"}
+        required_keys.add("rope_type")
+
+    missing_keys = required_keys - received_keys
+    if missing_keys:
+        raise KeyError(f"Missing required keys in `rope_scaling` for 'rope_type'='{rope_type}': {missing_keys}")
+
+    if optional_keys is not None:
+        unused_keys = received_keys - required_keys - optional_keys
+    else:
+        unused_keys = received_keys - required_keys
+    if unused_keys:
+        logger.warning(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
+
+
+def _validate_default_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+
+def _validate_linear_scaling_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+
+def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"original_max_position_embeddings"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+
+def _validate_yarn_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    optional_keys = {"attention_factor", "beta_fast", "beta_slow"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+    attention_factor = rope_scaling.get("attention_factor")
+    if attention_factor is not None and (not isinstance(attention_factor, float) or attention_factor < 0):
+        logger.warning(
+            f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
+        )
+    beta_fast = rope_scaling.get("beta_fast")
+    if beta_fast is not None and not isinstance(beta_fast, float):
+        logger.warning(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
+    beta_slow = rope_scaling.get("beta_slow")
+    if beta_slow is not None and not isinstance(beta_slow, float):
+        logger.warning(f"`rope_scaling`'s beta_slow field must be a float, got {beta_slow}")
+
+    if (beta_fast or 32) < (beta_slow or 1):
+        logger.warning(
+            f"`rope_scaling`'s beta_fast field must be greater than beta_slow, got beta_fast={beta_fast} "
+            f"(defaults to 32 if None) and beta_slow={beta_slow} (defaults to 1 if None)"
+        )
+
+
+def _validate_longrope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "short_factor", "long_factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"attention_factor", "factor", "original_max_position_embeddings"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+    dim = int(head_dim * partial_rotary_factor)
+
+    short_factor = rope_scaling.get("short_factor")
+    if not isinstance(short_factor, list) and all(isinstance(x, (int, float)) for x in short_factor):
+        logger.warning(f"`rope_scaling`'s short_factor field must be a list of numbers, got {short_factor}")
+    if not len(short_factor) == dim // 2:
+        logger.warning(f"`rope_scaling`'s short_factor field must have length {dim // 2}, got {len(short_factor)}")
+
+    long_factor = rope_scaling.get("long_factor")
+    if not isinstance(long_factor, list) and all(isinstance(x, (int, float)) for x in long_factor):
+        logger.warning(f"`rope_scaling`'s long_factor field must be a list of numbers, got {long_factor}")
+    if not len(long_factor) == dim // 2:
+        logger.warning(f"`rope_scaling`'s long_factor field must have length {dim // 2}, got {len(long_factor)}")
+
+    # Handle Phi3 divergence: prefer the use of `attention_factor` and/or `factor` over
+    # `original_max_position_embeddings` to compute internal variables. The latter lives outside `rope_scaling` and is
+    # unique to longrope (= undesirable)
+    if hasattr(config, "original_max_position_embeddings"):
+        logger.warning_once(
+            "This model has set a `original_max_position_embeddings` field, to be used together with "
+            "`max_position_embeddings` to determine a scaling factor. Please set the `factor` field of `rope_scaling`"
+            "with this ratio instead -- we recommend the use of this field over `original_max_position_embeddings`, "
+            "as it is compatible with most model architectures."
+        )
+    else:
+        factor = rope_scaling.get("factor")
+        if factor is None:
+            logger.warning("Missing required keys in `rope_scaling`: 'factor'")
+        elif not isinstance(factor, float) or factor < 1.0:
+            logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+        attention_factor = rope_scaling.get("attention_factor")
+        if attention_factor is not None:
+            if not isinstance(attention_factor, float) or attention_factor < 0.0:
+                logger.warning(
+                    f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
+                )
+
+
+def _validate_llama3_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor", "original_max_position_embeddings", "low_freq_factor", "high_freq_factor"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+    low_freq_factor = rope_scaling["low_freq_factor"]
+    high_freq_factor = rope_scaling["high_freq_factor"]
+    if low_freq_factor is None or not isinstance(low_freq_factor, float):
+        logger.warning(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
+    if high_freq_factor is None or not isinstance(high_freq_factor, float):
+        logger.warning(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
+    if high_freq_factor <= low_freq_factor:
+        logger.warning(
+            "`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
+            f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
+        )
+
+    original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
+    if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
+        logger.warning(
+            "`rope_scaling`'s original_max_position_embeddings field must be an integer, got "
+            f"{original_max_position_embeddings}"
+        )
+    if original_max_position_embeddings >= config.max_position_embeddings:
+        logger.warning(
+            "`rope_scaling`'s original_max_position_embeddings field must be less than max_position_embeddings, got "
+            f"{original_max_position_embeddings} and max_position_embeddings={config.max_position_embeddings}"
+        )
+
+
+# Like `ROPE_INIT_FUNCTIONS`, this validation function mapping can be dynamically updated for custom RoPE types.
+ROPE_VALIDATION_FUNCTIONS = {
+    "default": _validate_default_rope_parameters,
+    "linear": _validate_linear_scaling_rope_parameters,
+    "dynamic": _validate_dynamic_scaling_rope_parameters,
+    "yarn": _validate_yarn_parameters,
+    "longrope": _validate_longrope_parameters,
+    "llama3": _validate_llama3_parameters,
+}
+
+
+def rope_config_validation(config: PretrainedConfig):
+    """
+    Validate the RoPE config arguments, given a `PretrainedConfig` object
+    """
+    rope_scaling = getattr(config, "rope_scaling", None)  # not a default parameter in `PretrainedConfig`
+    if rope_scaling is None:
+        return
+
+    # BC: "rope_type" was originally "type"
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
+    validation_fn = ROPE_VALIDATION_FUNCTIONS.get(rope_type)
+    if validation_fn is not None:
+        validation_fn(config)
+    else:
+        logger.warning(
+            f"Missing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='{rope_type}'"
+        )