fix bug vllm integration

vllm_plugin_meralion/README.md

@@ -1,14 +1,14 @@
 ## MERaLiON-AudioLLM vLLM Serving
 
 > [!IMPORTANT]
-> MERaLiON-AudioLLM is trained on 30s audios. This vllm integration supports at most 4mins audio input. 
+> MERaLiON-AudioLLM is trained on 30 second audios. This vllm integration supports at most 4mins audio input. 
 
 ### Set up Environment
 
-MERaLiON-AudioLLM requires vLLM version `0.6.4.post1` and transformers `4.46.3`
+MERaLiON-AudioLLM requires vLLM version `6.4.post1` and transformers `4.46.3`
 
 ```bash
-pip install vllm==0.6.4.post1
+pip install vllm==6.4.post1
 pip install transformers==4.46.3
 ```
 
@@ -30,35 +30,27 @@ import torch
 from vllm import ModelRegistry, LLM, SamplingParams
 from vllm.assets.audio import AudioAsset
 
-def run_meralion(question: str):
-    model_name = "MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION"
+model_name = "MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION"
 
-    llm = LLM(model=model_name,
-              tokenizer=model_name,
-              max_num_seqs=8,
-              limit_mm_per_prompt={"audio": 1},
-              trust_remote_code=True,
-              dtype=torch.bfloat16
-              )
-
-    audio_in_prompt = "Given the following audio context: <SpeechHere>\n\n"
-
-    prompt = ("<start_of_turn>user\n"
-              f"{audio_in_prompt}Text instruction: {question}<end_of_turn>\n"
-              "<start_of_turn>model\n")
-    stop_token_ids = None
-    return llm, prompt, stop_token_ids
+llm = LLM(model=model_name,
+          tokenizer=model_name,
+          limit_mm_per_prompt={"audio": 1},
+          trust_remote_code=True,
+          dtype=torch.bfloat16
+          )
 
 audio_asset = AudioAsset("mary_had_lamb")
+
 question= "Please trancribe this speech."
+audio_in_prompt = "Given the following audio context: <SpeechHere>\n\n"
 
-llm, prompt, stop_token_ids = run_meralion(question)
+prompt = ("<start_of_turn>user\n"
+          f"{audio_in_prompt}Text instruction: {question}<end_of_turn>\n"
+          "<start_of_turn>model\n")
 
-# We set temperature to 0.2 so that outputs can be different
-# even when all prompts are identical when running batch inference.
 sampling_params = SamplingParams(
-  temperature=0.1,
-  top_p=0.9,
+  temperature=1,
+  top_p=9,
   top_k=50,
   repetition_penalty=1.1,
   seed=42,
@@ -86,7 +78,7 @@ Here is an example to start the server via the `vllm serve` command.
 ```bash
 export HF_TOKEN=<your-hf-token>
 
-vllm serve MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION --tokenizer MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION --max-num-seqs 8 --trust-remote-code --dtype bfloat16 --port 8000
+vllm serve MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION --tokenizer MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION --trust-remote-code --dtype bfloat16 --port 8000
 ```
 
 To call the server, you can use the [official OpenAI client](https://github.com/openai/openai-python):
@@ -147,8 +139,8 @@ client, model_name = get_client(base_url="http://localhost:8000/v1")
 generation_parameters = dict(
     model=model_name,
     max_completion_tokens=1024,
-    temperature=0.1,
-    top_p=0.9,
+    temperature=1,
+    top_p=9,
     extra_body={
         "repetition_penalty": 1.1,
         "top_k": 50,
@@ -178,8 +170,138 @@ curl http://localhost:8000/v1/chat/completions \
             }
         ],
         "max_completion_tokens": 1024,
-        "temperature": 0.1, 
-        "top_p": 0.9, 
+        "temperature": 1, 
+        "top_p": 9, 
         "seed": 42 
     }'
-```
+```
+
+
+### Inference Performance Benchmark
+
+We report average **Time To First Token** (**TTFT**, unit: ms) together with **Inter-Token Latency** (**ITL**, unit: ms) with vLLM instance running on H100 and A100 GPU respectively.  
+
+Input: 120 speech recognition prompts for each input audio length and concurrency combination.\
+Output: The corresponding output length of these prompts.
+
+
+<p style="text-align: center;"><strong>Single NVIDIA H100 GPU (80GiB GPU memory)</strong></p>
+
+<table style="margin: 0px auto;">
+  <thead>
+    <tr>
+      <th>Input Audio Length</th>
+      <th style="text-align: center;" colspan="2">30s</th>
+      <th style="text-align: center;" colspan="2">1min</th>
+      <th style="text-align: center;" colspan="2">2mins</th>
+    </tr>
+    <tr>
+      <th>Concurrent requests</th>
+      <th>TTFT (ms)</th>
+      <th>ITL (ms)</th>
+      <th>TTFT (ms)</th>
+      <th>ITL (ms)</th>
+      <th>TTFT (ms)</th>
+      <th>ITL (ms)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>1</td>
+      <td>85.8</td>
+      <td>9.9</td>
+      <td>126.4</td>
+      <td>9.6</td>
+      <td>214.5</td>
+      <td>9.7</td>
+    </tr>
+    <tr>
+      <td>4</td>
+      <td>96.9</td>
+      <td>11.4</td>
+      <td>159.6</td>
+      <td>11.1</td>
+      <td>258.1</td>
+      <td>11.2</td>
+    </tr>
+    <tr>
+      <td>8</td>
+      <td>109.6</td>
+      <td>13.0</td>
+      <td>206.5</td>
+      <td>12.7</td>
+      <td>261.9</td>
+      <td>13.0</td>
+    </tr>
+    <tr>
+      <td>16</td>
+      <td>149.9</td>
+      <td>16.3</td>
+      <td>236.7</td>
+      <td>16.2</td>
+      <td>299.0</td>
+      <td>16.8</td>
+    </tr>
+  </tbody>
+</table>
+
+<p style="text-align: center;"><strong>Single NVIDIA A100 GPU (40GiB GPU memory)</strong></p>
+
+<table style="margin: 0px auto;">
+  <thead>
+    <tr>
+      <th>Input Audio Length</th>
+      <th style="text-align: center;" colspan="2">30s</th>
+      <th style="text-align: center;" colspan="2">1min</th>
+      <th style="text-align: center;" colspan="2">2mins</th>
+    </tr>
+    <tr>
+      <th>Concurrent requests</th>
+      <th>TTFT (ms)</th>
+      <th>ITL (ms)</th>
+      <th>TTFT (ms)</th>
+      <th>ITL (ms)</th>
+      <th>TTFT (ms)</th>
+      <th>ITL (ms)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>1</td>
+      <td>162.6</td>
+      <td>18.0</td>
+      <td>195.0</td>
+      <td>18.3</td>
+      <td>309.9</td>
+      <td>18.6</td>
+    </tr>
+    <tr>
+      <td>4</td>
+      <td>159.1</td>
+      <td>21.1</td>
+      <td>226.9</td>
+      <td>21.2</td>
+      <td>329.5</td>
+      <td>21.6</td>
+    </tr>
+    <tr>
+      <td>8</td>
+      <td>176.5</td>
+      <td>25.2</td>
+      <td>305.4</td>
+      <td>24.8</td>
+      <td>352.5</td>
+      <td>25.5</td>
+    </tr>
+    <tr>
+      <td>16</td>
+      <td>196.0</td>
+      <td>32.0</td>
+      <td>329.4</td>
+      <td>31.9</td>
+      <td>414.7</td>
+      <td>33.4</td>
+    </tr>
+  </tbody>
+</table>
+

vllm_plugin_meralion/change_log.md

@@ -0,0 +1,5 @@
+### 0.3.0
+- Optimize audio processing code
+- Fix bug about huggingface attention implementation autoset. Better compatibility with transformers > 4.45.2
+- Support LoRA adaptors on the text decoder module.
+- Support embedding task

vllm_plugin_meralion/{set_up.py → setup.py}

@@ -1,7 +1,7 @@
 from setuptools import setup
 
 setup(name='vllm_plugin_meralion',
-      version='0.2',
+      version='0.3.0',
       packages=['vllm_plugin_meralion'],
       entry_points={
           'vllm.general_plugins':

vllm_plugin_meralion/vllm_plugin_meralion/configuration_meralion.py

@@ -1,505 +0,0 @@
-"""MERaLiON AudioLLM model configuration"""
-
-from collections import OrderedDict
-from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.onnx import OnnxConfig
-from transformers.utils import logging
-
-
-if TYPE_CHECKING:
-    from transformers.feature_extraction_utils import FeatureExtractionMixin
-    from transformers.tokenization_utils_base import PreTrainedTokenizerBase
-    from transformers.utils import TensorType
-
-
-logger = logging.get_logger(__name__)
-
-
-# fmt: off
-NON_SPEECH_TOKENS = [
-    1, 2, 7, 8, 9, 10, 14, 25,
-    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
-    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
-    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
-    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
-    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
-    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
-    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
-    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
-]
-NON_SPEECH_TOKENS_MULTI = [
-    1, 2, 7, 8, 9, 10, 14, 25,
-    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
-    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
-    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
-    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
-    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
-    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
-    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
-    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
-]
-# fmt: on
-
-# Copied from transformers.models.whisper.configuration_whisper.WhisperConfig
-class MERaLiONSpeechConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MERaLiONSpeechModel`]. It is used to instantiate a
-    MERaLiONSpeech model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the MERaLiONSpeech
-    [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 51865):
-            Vocabulary size of the MERaLiONSpeech model. Defines the number of different tokens that can be represented by the
-            `decoder_input_ids` passed when calling [`MERaLiONSpeechModel`]
-        num_mel_bins (`int`, *optional*, defaults to 80):
-            Number of mel features used per input features. Should correspond to the value used in the
-            `MERaLiONSpeechProcessor` class.
-        encoder_layers (`int`, *optional*, defaults to 4):
-            Number of encoder layers.
-        decoder_layers (`int`, *optional*, defaults to 4):
-            Number of decoder layers.
-        encoder_attention_heads (`int`, *optional*, defaults to 6):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        decoder_attention_heads (`int`, *optional*, defaults to 6):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        encoder_ffn_dim (`int`, *optional*, defaults to 1536):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
-        decoder_ffn_dim (`int`, *optional*, defaults to 1536):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
-        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
-            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
-            for more details.
-        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
-            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
-            for more details.
-        decoder_start_token_id (`int`, *optional*, defaults to 50257):
-            Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
-            are provided to the `generate` function. It is used to guide the model`s generation process depending on
-            the task.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models).
-        is_encoder_decoder (`bool`, *optional*, defaults to `True`):
-            Whether the model is used as an encoder/decoder or not.
-        activation_function (`str`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"silu"` and `"gelu_new"` are supported.
-        d_model (`int`, *optional*, defaults to 384):
-            Dimensionality of the layers.
-        dropout (`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        activation_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for activations inside the fully connected layer.
-        init_std (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        scale_embedding (`bool`, *optional*, defaults to False):
-            Scale embeddings by diving by sqrt(d_model).
-        max_source_positions (`int`, *optional*, defaults to 1500):
-            The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
-        max_target_positions (`int`, *optional*, defaults to 448):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        pad_token_id (`int`, *optional*, defaults to 50256):
-            Padding token id.
-        bos_token_id (`int`, *optional*, defaults to 50256):
-            Begin of stream token id.
-        eos_token_id (`int`, *optional*, defaults to 50256):
-            End of stream token id.
-        suppress_tokens (`List[int]`, *optional*):
-            A list containing the non-speech tokens that will be used by the logit processor in the `generate`
-            function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the
-            `multilingual` model.
-        begin_suppress_tokens (`List[int]`, *optional*, defaults to `[220,50256]`):
-            A list containing tokens that will be supressed at the beginning of the sampling process. Initialized as
-            the token for `" "` (`blank_token_id`) and the `eos_token_id`
-        use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
-            Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
-            instance of [`MERaLiONSpeechForAudioClassification`].
-        classifier_proj_size (`int`, *optional*, defaults to 256):
-            Dimensionality of the projection before token mean-pooling for classification. Only relevant when using an
-            instance of [`MERaLiONSpeechForAudioClassification`].
-        apply_spec_augment (`bool`, *optional*, defaults to `False`):
-            Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
-            [SpecAugment: A Simple Data Augmentation Method for Automatic Speech
-            Recognition](https://arxiv.org/abs/1904.08779).
-        mask_time_prob (`float`, *optional*, defaults to 0.05):
-            Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
-            procecure generates `mask_time_prob*len(time_axis)/mask_time_length` independent masks over the axis. If
-            reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
-            masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
-            actual percentage of masked vectors. This is only relevant if `apply_spec_augment == True`.
-        mask_time_length (`int`, *optional*, defaults to 10):
-            Length of vector span along the time axis.
-        mask_time_min_masks (`int`, *optional*, defaults to 2),:
-            The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
-            irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
-            mask_time_min_masks''
-        mask_feature_prob (`float`, *optional*, defaults to 0.0):
-            Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
-            masking procecure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over
-            the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
-            span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
-            may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
-            True`.
-        mask_feature_length (`int`, *optional*, defaults to 10):
-            Length of vector span along the feature axis.
-        mask_feature_min_masks (`int`, *optional*, defaults to 0),:
-            The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
-            step, irrespectively of `mask_feature_prob`. Only relevant if
-            `mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks`.
-        median_filter_width (`int`, *optional*, defaults to 7):
-            Width of the median filter used to smoothen to cross-attention outputs when computing token timestamps.
-            Should be an odd number.
-    """
-
-    model_type = "meralion_speech_encoder"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {
-        "num_key_value_heads": "encoder_attention_heads",
-        "num_attention_heads": "encoder_attention_heads",
-        "hidden_size": "d_model",
-    }
-
-    def __init__(
-        self,
-        vocab_size=51865,
-        num_mel_bins=80,
-        encoder_layers=4,
-        encoder_attention_heads=6,
-        decoder_layers=4,
-        decoder_attention_heads=6,
-        decoder_ffn_dim=1536,
-        encoder_ffn_dim=1536,
-        encoder_layerdrop=0.0,
-        decoder_layerdrop=0.0,
-        decoder_start_token_id=50257,
-        use_cache=True,
-        is_encoder_decoder=True,
-        activation_function="gelu",
-        d_model=384,
-        dropout=0.0,
-        attention_dropout=0.0,
-        activation_dropout=0.0,
-        init_std=0.02,
-        scale_embedding=False,
-        max_source_positions=1500,
-        max_target_positions=448,
-        pad_token_id=50256,
-        bos_token_id=50256,
-        eos_token_id=50256,
-        suppress_tokens=None,
-        begin_suppress_tokens=[220, 50256],
-        use_weighted_layer_sum=False,
-        classifier_proj_size=256,
-        apply_spec_augment=False,
-        mask_time_prob=0.05,
-        mask_time_length=10,
-        mask_time_min_masks=2,
-        mask_feature_prob=0.0,
-        mask_feature_length=10,
-        mask_feature_min_masks=0,
-        median_filter_width=7,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.num_mel_bins = num_mel_bins
-        self.d_model = d_model
-        self.encoder_layers = encoder_layers
-        self.encoder_attention_heads = encoder_attention_heads
-        self.decoder_layers = decoder_layers
-        self.decoder_attention_heads = decoder_attention_heads
-        self.decoder_ffn_dim = decoder_ffn_dim
-        self.encoder_ffn_dim = encoder_ffn_dim
-        self.dropout = dropout
-        self.attention_dropout = attention_dropout
-        self.activation_dropout = activation_dropout
-        self.activation_function = activation_function
-        self.init_std = init_std
-        self.encoder_layerdrop = encoder_layerdrop
-        self.decoder_layerdrop = decoder_layerdrop
-        self.use_cache = use_cache
-        self.num_hidden_layers = encoder_layers
-        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
-        self.max_source_positions = max_source_positions
-        self.max_target_positions = max_target_positions
-
-        # Audio Classification-specific parameters. Feel free to ignore for other classes.
-        self.classifier_proj_size = classifier_proj_size
-        self.use_weighted_layer_sum = use_weighted_layer_sum
-
-        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
-        self.apply_spec_augment = apply_spec_augment
-        self.mask_time_prob = mask_time_prob
-        self.mask_time_length = mask_time_length
-        self.mask_time_min_masks = mask_time_min_masks
-        self.mask_feature_prob = mask_feature_prob
-        self.mask_feature_length = mask_feature_length
-        self.mask_feature_min_masks = mask_feature_min_masks
-
-        self.median_filter_width = median_filter_width
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            is_encoder_decoder=is_encoder_decoder,
-            decoder_start_token_id=decoder_start_token_id,
-            suppress_tokens=suppress_tokens,
-            begin_suppress_tokens=begin_suppress_tokens,
-            **kwargs,
-        )
-    @property
-    def inputs(self) -> Mapping[str, Mapping[int, str]]:
-        common_inputs = OrderedDict(
-            [
-                ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
-            ]
-        )
-        if self.use_past:
-            common_inputs["decoder_input_ids"] = {0: "batch"}
-        else:
-            common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
-
-        if self.use_past:
-            self.fill_with_past_key_values_(common_inputs, direction="inputs")
-
-        return common_inputs
-
-    def generate_dummy_inputs(
-        self,
-        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
-        batch_size: int = -1,
-        seq_length: int = -1,
-        is_pair: bool = False,
-        framework: Optional["TensorType"] = None,
-        sampling_rate: int = 22050,
-        time_duration: float = 5.0,
-        frequency: int = 220,
-    ) -> Mapping[str, Any]:
-        dummy_inputs = OrderedDict()
-        encoder_inputs = OnnxConfig.generate_dummy_inputs(
-            self,
-            preprocessor=preprocessor.feature_extractor,
-            batch_size=batch_size,
-            framework=framework,
-            sampling_rate=sampling_rate,
-            time_duration=time_duration,
-            frequency=frequency,
-        )
-        encoder_sequence_length = encoder_inputs["input_features"].shape[2]
-        seq_length = encoder_sequence_length // 2 if self.use_past else seq_length
-
-        decoder_inputs = super().generate_dummy_inputs(
-            preprocessor.tokenizer, batch_size, seq_length, is_pair, framework
-        )
-
-        dummy_inputs["input_features"] = encoder_inputs.pop("input_features")
-        dummy_inputs["decoder_input_ids"] = decoder_inputs.pop("decoder_input_ids")
-
-        if "past_key_values" in decoder_inputs:
-            dummy_inputs["past_key_values"] = decoder_inputs.pop("past_key_values")
-
-        return dummy_inputs
-
-    @property
-    def atol_for_validation(self) -> float:
-        return 1e-3
-    
-
-# Copied from transformers.models.gemma2.configuration_gemma2.Gemma2Config
-class MERaLiONTextConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MERaLiONTextModel`]. It is used to instantiate an MERaLiONText
-    model according to the specified arguments, defining the model architecture. 
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-    Args:
-        vocab_size (`int`, *optional*, defaults to 256000):
-            Vocabulary size of the MERaLiONText model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`MERaLiONTextModel`]
-        hidden_size (`int`, *optional*, defaults to 3072):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 24576):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 28):
-            Number of hidden layers in the Transformer decoder.
-        num_attention_heads (`int`, *optional*, defaults to 16):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        num_key_value_heads (`int`, *optional*, defaults to 16):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
-            `num_attention_heads`.
-        head_dim (`int`, *optional*, defaults to 256):
-            The attention head dimension.
-        hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
-            The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
-            if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
-        max_position_embeddings (`int`, *optional*, defaults to 8192):
-            The maximum sequence length that this model might ever be used with.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        pad_token_id (`int`, *optional*, defaults to 0):
-            Padding token id.
-        eos_token_id (`int`, *optional*, defaults to 1):
-            End of stream token id.
-        bos_token_id (`int`, *optional*, defaults to 2):
-            Beginning of stream token id.
-        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
-            Whether to tie weight embeddings
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
-            Whether to use a bias in the query, key, value and output projection layers during self-attention.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        query_pre_attn_scalar (`float`, *optional*, defaults to 224): scaling factor used on the attention scores
-        sliding_window (`int`, *optional*, defaults to 4096): in MERaLiONText, every other layer uses sliding window attention. This is the
-            size of the sliding window.
-        final_logit_softcapping (`float`, *optional*, defaults to 30.0): scaling factor when applying tanh softcapping on the logits.
-        attn_logit_softcapping (`float`, *optional*, defaults to 50.0): scaling factor when applying tanh softcapping on the attention scores.
-        cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`.
-    """
-
-    model_type = "meralion_text_decoder"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=256000,
-        hidden_size=3072,
-        intermediate_size=24576,
-        num_hidden_layers=28,
-        num_attention_heads=16,
-        num_key_value_heads=16,
-        head_dim=256,
-        hidden_activation="gelu_pytorch_tanh",
-        max_position_embeddings=8192,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=0,
-        eos_token_id=1,
-        bos_token_id=2,
-        tie_word_embeddings=True,
-        rope_theta=10000.0,
-        attention_bias=False,
-        attention_dropout=0.0,
-        query_pre_attn_scalar=224,
-        sliding_window=4096,
-        final_logit_softcapping=30.0,
-        attn_logit_softcapping=50.0,
-        cache_implementation="hybrid",
-        **kwargs,
-    ):
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-        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.head_dim = head_dim
-        self.num_key_value_heads = num_key_value_heads
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        self.hidden_activation = hidden_activation
-        self.query_pre_attn_scalar = query_pre_attn_scalar
-        self.sliding_window = sliding_window
-        self.final_logit_softcapping = final_logit_softcapping
-        self.attn_logit_softcapping = attn_logit_softcapping
-        self.cache_implementation = cache_implementation
-
-
-class MERaLiONConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MERaLiONForConditionalGeneration`]. It is used to instantiate an
-    MERaLiON model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the MERaLiON.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        audio_config (`Union[AutoConfig, dict]`,  *optional*, defaults to `CLIPVisionConfig`):
-            The config object or dictionary of the audio backbone.
-        text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
-            The config object or dictionary of the text backbone.
-        audio_token_index (`int`, *optional*, defaults to 151646):
-            The image token index to encode the image prompt.
-    """
-
-    model_type = "meralion"
-    is_composition = False
-
-    def __init__(
-        self,
-        speech_config=None,
-        text_config=None,
-        speech_mlp_scale_factor=15,
-        speech_token_index=255999,
-        **kwargs,
-    ):
-        
-        if isinstance(speech_config, dict):
-            speech_config = MERaLiONSpeechConfig(**speech_config)
-        elif speech_config is None:
-            speech_config = MERaLiONSpeechConfig(
-                d_model=1280,
-                encoder_attention_heads=20,
-                encoder_ffn_dim=5120,
-                encoder_layerdrop=0.0,
-                encoder_layers=32,
-                num_mel_bins=128,
-                max_source_positions=1500,
-                scale_embedding=False,
-                activation_function="gelu",
-            )
-
-        self.speech_config = speech_config
-
-        if isinstance(text_config, dict):
-            text_config = MERaLiONTextConfig(**text_config)
-        elif text_config is None:
-            text_config = MERaLiONTextConfig()
-
-        self.text_config = text_config
-
-        self.speech_mlp_scale_factor = speech_mlp_scale_factor
-        self.speech_token_index = speech_token_index
-        
-        self.sliding_window = self.text_config.sliding_window
-        self.hidden_size = self.text_config.hidden_size
-        self.num_attention_heads = self.text_config.num_attention_heads
-        self.num_hidden_layers = self.text_config.num_hidden_layers
-        self.num_key_value_heads = self.text_config.num_key_value_heads
-        self.head_dim = self.text_config.head_dim
-        self.intermediate_size = self.text_config.intermediate_size
-        
-        super().__init__(**kwargs)

vllm_plugin_meralion/vllm_plugin_meralion/modeling_meralion.py

@@ -1,1306 +0,0 @@
-"""PyTorch MERaLiON AudioLLM model."""
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import EncoderDecoderCache, StaticCache, HybridCache
-from transformers.generation import GenerationMixin
-from transformers.modeling_outputs import ModelOutput, BaseModelOutput
-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 .configuration_meralion import MERaLiONConfig, MERaLiONSpeechConfig
-from .modeling_text_decoder import MERaLiONTextForCausalLM
-
-
-if is_flash_attn_2_available():
-    from transformers.modeling_flash_attention_utils import _flash_attention_forward
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "MERaLiONConfig"
-
-
-def sinusoids(length: int, channels: int, max_timescale: float = 10000) -> torch.Tensor:
-    """Returns sinusoids for positional embedding"""
-    if channels % 2 != 0:
-        raise ValueError(
-            f"Number of channels has to be divisible by 2 for sinusoidal positional embeddings, got {channels} channels."
-        )
-    log_timescale_increment = math.log(max_timescale) / (channels // 2 - 1)
-    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
-    scaled_time = torch.arange(length).view(-1, 1) * inv_timescales.view(1, -1)
-    return torch.cat([scaled_time.sin(), scaled_time.cos()], dim=1)
-
-
-# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
-def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
-    """
-    Shift input ids one token to the right.
-    """
-    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
-    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
-    shifted_input_ids[:, 0] = decoder_start_token_id
-
-    if pad_token_id is None:
-        raise ValueError("self.model.config.pad_token_id has to be defined.")
-    # replace possible -100 values in labels by `pad_token_id`
-    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
-
-    return shifted_input_ids
-
-
-# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
-def _prepare_4d_causal_attention_mask_with_cache_position(
-    attention_mask: torch.Tensor,
-    sequence_length: int,
-    target_length: int,
-    dtype: torch.dtype,
-    device: torch.device,
-    min_dtype: float,
-    cache_position: torch.Tensor,
-    batch_size: int,
-):
-    """
-    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
-    `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
-
-    Args:
-        attention_mask (`torch.Tensor`):
-            A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
-        sequence_length (`int`):
-            The sequence length being processed.
-        target_length (`int`):
-            The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
-        dtype (`torch.dtype`):
-            The dtype to use for the 4D attention mask.
-        device (`torch.device`):
-            The device to plcae the 4D attention mask on.
-        min_dtype (`float`):
-            The minimum value representable with the dtype `dtype`.
-        cache_position (`torch.Tensor`):
-            Indices depicting the position of the input sequence tokens in the sequence.
-        batch_size (`torch.Tensor`):
-            Batch size.
-    """
-    if attention_mask is not None and attention_mask.dim() == 4:
-        # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
-        causal_mask = attention_mask
-    else:
-        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
-        if sequence_length != 1:
-            causal_mask = torch.triu(causal_mask, diagonal=1)
-        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
-        if attention_mask is not None:
-            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-            mask_length = attention_mask.shape[-1]
-            padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
-            padding_mask = padding_mask == 0
-            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                padding_mask, min_dtype
-            )
-    return causal_mask
-
-
-class MERaLiONSpeechAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        dropout: float = 0.0,
-        is_decoder: bool = False,
-        bias: bool = True,
-        is_causal: bool = False,
-        layer_idx: Optional[int] = None,
-        config: Optional[MERaLiONSpeechConfig] = None,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-        self.config = config
-
-        if (self.head_dim * num_heads) != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
-                f" and `num_heads`: {num_heads})."
-            )
-        self.scaling = self.head_dim**-0.5
-        self.is_decoder = is_decoder
-        self.is_causal = is_causal
-
-        if layer_idx is None and is_decoder:
-            logger.warning_once(
-                f"Instantiating a decoder {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.layer_idx = layer_idx
-
-        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
-        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-
-    # Copied from transformers.models.bart.modeling_bart.BartAttention._shape with BART->speech
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        key_value_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[EncoderDecoderCache] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-        bsz, tgt_len, _ = hidden_states.size()
-
-        # get query proj
-        query_states = self._shape(self.q_proj(hidden_states) * self.scaling, tgt_len, bsz)
-
-        if past_key_value is not None:
-            is_updated = past_key_value.is_updated.get(self.layer_idx)
-            if is_cross_attention:
-                # after the first generated id, we can subsequently re-use all key/value_states from cache
-                past_key_value.is_updated[self.layer_idx] = True
-                past_key_value = past_key_value.cross_attention_cache
-            else:
-                past_key_value = past_key_value.self_attention_cache
-
-        # use key_value_states if cross attention
-        current_states = key_value_states if key_value_states is not None else hidden_states
-        if is_cross_attention and past_key_value and is_updated:
-            # reuse k,v, cross_attentions
-            key_states = past_key_value.key_cache[self.layer_idx]
-            value_states = past_key_value.value_cache[self.layer_idx]
-        else:
-            key_states = self._shape(self.k_proj(current_states), -1, bsz)
-            value_states = self._shape(self.v_proj(current_states), -1, bsz)
-            if past_key_value is not None:
-                # save all key/value_states to cache to be re-used for fast auto-regressive generation
-                cache_position = cache_position if not is_cross_attention else None
-                key_states, value_states = past_key_value.update(
-                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
-                )
-
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
-
-        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
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if layer_head_mask is not None:
-            if layer_head_mask.size() != (self.num_heads,):
-                raise ValueError(
-                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
-                    f" {layer_head_mask.size()}"
-                )
-            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-        attn_output = torch.matmul(attn_probs, value_states)
-
-        if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.transpose(1, 2)
-        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
-        # partitioned across GPUs when using tensor-parallelism.
-        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights, past_key_value
-
-
-class MERaLiONSpeechFlashAttention2(MERaLiONSpeechAttention):
-    """
-    MERaLiONSpeech flash attention module. This module inherits from `MERaLiONSpeechAttention` as the weights of the module stays
-    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
-    flash attention and deal with padding tokens in case the input contains any of them.
-    """
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        key_value_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[EncoderDecoderCache] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if isinstance(past_key_value, StaticCache):
-            raise ValueError(
-                "The `static` cache implementation is not compatible with `attn_implementation='flash_attention_2'`. "
-                "Use `attn_implementation='sdpa'` in the meantime, and open an issue at https://github.com/huggingface/transformers"
-            )
-        # SpeechFlashAttention2 attention does not support output_attentions
-        if output_attentions:
-            raise ValueError("SpeechFlashAttention2 attention does not support output_attentions")
-
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-        bsz, tgt_len, _ = hidden_states.size()
-
-        # get query proj
-        query_states = torch.reshape(self.q_proj(hidden_states), (bsz, tgt_len, self.num_heads, self.head_dim))
-
-        if past_key_value is not None:
-            is_updated = past_key_value.is_updated.get(self.layer_idx)
-            if is_cross_attention:
-                # after the first generated id, we can subsequently re-use all key/value_states from cache
-                past_key_value.is_updated[self.layer_idx] = True
-                past_key_value = past_key_value.cross_attention_cache
-            else:
-                past_key_value = past_key_value.self_attention_cache
-
-        # use key_value_states if cross attention
-        current_states = key_value_states if key_value_states is not None else hidden_states
-        if is_cross_attention and past_key_value and is_updated:
-            # reuse k,v, cross_attentions
-            key_states = past_key_value.key_cache[self.layer_idx]
-            value_states = past_key_value.value_cache[self.layer_idx]
-        else:
-            key_states = self._shape(self.k_proj(current_states), -1, bsz)
-            value_states = self._shape(self.v_proj(current_states), -1, bsz)
-            if past_key_value is not None:
-                # save all key/value_states to cache to be re-used for fast auto-regressive generation
-                cache_position = cache_position if not is_cross_attention else None
-                key_states, value_states = past_key_value.update(
-                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
-                )
-
-        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]
-        #  We would need to refactor the KV cache to be able to avoid many of these transpose/reshape/view.
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        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]]
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in the correct dtype just to be sure everything works as expected.
-        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
-        # in fp32. (LlamaRMSNorm handles it correctly)
-
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.q_proj.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        attn_output = _flash_attention_forward(
-            query_states,
-            key_states,
-            value_states,
-            causal_mask,
-            tgt_len,
-            dropout=self.dropout if self.training else 0.0,
-            is_causal=self.is_causal,
-            use_top_left_mask=self._flash_attn_uses_top_left_mask,
-        )
-
-        attn_output = attn_output.reshape(bsz, tgt_len, -1)
-        attn_output = self.out_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class MERaLiONSpeechSdpaAttention(MERaLiONSpeechAttention):
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        key_value_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[EncoderDecoderCache] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-        if output_attentions or layer_head_mask is not None:
-            # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented.
-            logger.warning_once(
-                "MERaLiONSpeechModel is using MERaLiONSpeechSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. 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,
-                key_value_states=key_value_states,
-                past_key_value=past_key_value,
-                attention_mask=attention_mask,
-                layer_head_mask=layer_head_mask,
-                output_attentions=output_attentions,
-                cache_position=cache_position,
-            )
-
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-        bsz, tgt_len, _ = hidden_states.size()
-
-        # get query proj
-        query_states = self._shape(self.q_proj(hidden_states), tgt_len, bsz)
-
-        if past_key_value is not None:
-            is_updated = past_key_value.is_updated.get(self.layer_idx)
-            if is_cross_attention:
-                # after the first generated id, we can subsequently re-use all key/value_states from cache
-                past_key_value.is_updated[self.layer_idx] = True
-                past_key_value = past_key_value.cross_attention_cache
-            else:
-                past_key_value = past_key_value.self_attention_cache
-
-        # use key_value_states if cross attention
-        current_states = key_value_states if key_value_states is not None else hidden_states
-        if is_cross_attention and past_key_value and is_updated:
-            # reuse k,v, cross_attentions
-            key_states = past_key_value.key_cache[self.layer_idx]
-            value_states = past_key_value.value_cache[self.layer_idx]
-        else:
-            key_states = self._shape(self.k_proj(current_states), -1, bsz)
-            value_states = self._shape(self.v_proj(current_states), -1, bsz)
-            if past_key_value is not None:
-                # save all key/value_states to cache to be re-used for fast auto-regressive generation
-                cache_position = cache_position if not is_cross_attention else None
-                key_states, value_states = past_key_value.update(
-                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
-                )
-
-        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]]
-
-        # 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 tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1.
-        is_causal = True if self.is_causal and causal_mask is None and tgt_len > 1 else False
-
-        # NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask,
-        # but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
-        attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_states,
-            key_states,
-            value_states,
-            attn_mask=causal_mask,
-            dropout_p=self.dropout if self.training else 0.0,
-            is_causal=is_causal,
-        )
-
-        if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.transpose(1, 2)
-
-        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
-        # partitioned across GPUs when using tensor-parallelism.
-        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, None, past_key_value
-
-
-MERALION_SPEECH_ATTENTION_CLASSES = {
-    "eager": MERaLiONSpeechAttention,
-    "flash_attention_2": MERaLiONSpeechFlashAttention2,
-    "sdpa": MERaLiONSpeechSdpaAttention,
-}
-
-
-# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->Speech, MBART->WHISPER
-class MERaLiONSpeechEncoderLayer(nn.Module):
-    def __init__(self, config: MERaLiONSpeechConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-
-        self.self_attn = MERALION_SPEECH_ATTENTION_CLASSES[config._attn_implementation](
-            embed_dim=self.embed_dim,
-            num_heads=config.encoder_attention_heads,
-            dropout=config.attention_dropout,
-            config=config,
-        )
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
-        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        layer_head_mask: torch.Tensor,
-        output_attentions: bool = False,
-    ) -> torch.Tensor:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
-                `(encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-        hidden_states, attn_weights, _ = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            layer_head_mask=layer_head_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-
-        if hidden_states.dtype == torch.float16 and (
-            torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
-        ):
-            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
-            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-class MERaLiONSpeechPreTrainedModel(PreTrainedModel):
-    config_class = MERaLiONSpeechConfig
-    base_model_prefix = "model"
-    main_input_name = "input_features"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["MERaLiONSpeechEncoderLayer", "MERaLiONSpeechDecoderLayer"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-    _supports_static_cache = True
-
-    def _init_weights(self, module):
-        std = self.config.init_std
-        if isinstance(module, (nn.Linear, nn.Conv1d)):
-            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_()
-        elif isinstance(module, MERaLiONSpeechEncoder):
-            with torch.no_grad():
-                embed_positions = module.embed_positions.weight
-                embed_positions.copy_(sinusoids(*embed_positions.shape))
-
-    def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
-        """
-        Computes the output length of the convolutional layers
-        """
-        input_lengths = (input_lengths - 1) // 2 + 1
-
-        return input_lengths
-
-
-MERALION_SPEECH_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 ([`MERaLiONSpeechConfig`]):
-            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.
-"""
-
-MERALION_SPEECH_INPUTS_DOCSTRING = r"""
-    Args:
-        input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`):
-            Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
-            loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
-            tensor of type `torch.FloatTensor`. See [`~SpeechFeatureExtractor.__call__`]
-        attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing *SpecAugment* data augmentation 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)
-        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Indices of decoder input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`SpeechTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are decoder input IDs?](../glossary#decoder-input-ids)
-
-            Speech uses the `decoder_start_token_id` as the starting token for `decoder_input_ids` generation. If
-            `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
-            `past_key_values`).
-        decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
-            be used by default.
-
-            If you want to change padding behavior, you should read
-            [`modeling_speech._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the BART
-            paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
-        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
-            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
-            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
-        past_key_values (`EncoderDecoderCache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states that can be used to speed up auto-regressive (sequential) decoding. There are
-            four sets of pre-computed hidden-states: key and values states in the self-attention blocks (2) and
-            in the cross-attention blocks (2). The `past_key_values` are returned when `use_cache=True` is passed or
-            when `config.use_cache=True`
-
-            Two formats are allowed:
-            - An [`~cache_utils.EncoderDecoderCache`] instance;
-            - 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)`.
-
-            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)`.
-        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
-            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
-            input (see `past_key_values`). This is useful if you want more control over how to convert
-            `decoder_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.
-        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-            Indices depicting the position of the input sequence tokens in the sequence. It is used to update the cache
-            in the correct position and to infer the complete sequence length.
-"""
-
-MERALION_SPEECH_ENCODER_INPUTS_DOCSTRING = r"""
-    Args:
-        input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`):
-            Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
-            loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
-            tensor of type `torch.FloatTensor`. See [`~SpeechFeatureExtractor.__call__`]
-        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
-            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
-            hidden-states at the output of the last layer of the encoder.
-        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.
-"""
-
-
-class MERaLiONSpeechEncoder(MERaLiONSpeechPreTrainedModel):
-    """
-    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
-    [`MERaLiONSpeechEncoderLayer`].
-
-    Args:
-        config: MERaLiONSpeechConfig
-    """
-
-    def __init__(self, config: MERaLiONSpeechConfig):
-        super().__init__(config)
-        self.dropout = config.dropout
-        self.layerdrop = config.encoder_layerdrop
-
-        embed_dim = config.d_model
-        self.num_mel_bins = config.num_mel_bins
-        self.padding_idx = config.pad_token_id
-        self.max_source_positions = config.max_source_positions
-        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
-
-        self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1)
-        self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1)
-
-        self.embed_positions = nn.Embedding(self.max_source_positions, embed_dim)
-        self.embed_positions.requires_grad_(False)
-
-        self.layers = nn.ModuleList([MERaLiONSpeechEncoderLayer(config) for _ in range(config.encoder_layers)])
-        self.layer_norm = nn.LayerNorm(config.d_model)
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def _freeze_parameters(self):
-        for param in self.parameters():
-            param.requires_grad = False
-        self._requires_grad = False
-
-    def get_input_embeddings(self) -> nn.Module:
-        return self.conv1
-
-    def set_input_embeddings(self, value: nn.Module):
-        self.conv1 = value
-
-    def forward(
-        self,
-        input_features,
-        attention_mask=None,
-        head_mask=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        Args:
-            input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
-                Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
-                obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a
-                `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
-                `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
-                and conversion into a tensor of type `torch.FloatTensor`. See [`~SpeechFeatureExtractor.__call__`]
-            attention_mask (`torch.Tensor`)`, *optional*):
-                Speech does not support masking of the `input_features`, this argument is preserved for compatibility,
-                but it is not used. By default the silence in the input log mel spectrogram are ignored.
-            head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-            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.
-        """
-
-        expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0]
-        if input_features.shape[-1] != expected_seq_length:
-            raise ValueError(
-                f"Speech expects the mel input features to be of length {expected_seq_length}, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
-            )
-
-        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
-        inputs_embeds = nn.functional.gelu(self.conv1(input_features))
-        inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
-
-        inputs_embeds = inputs_embeds.permute(0, 2, 1)
-        embed_pos = self.embed_positions.weight
-
-        hidden_states = inputs_embeds + embed_pos
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        # check if head_mask has a correct number of layers specified if desired
-        if head_mask is not None:
-            assert head_mask.size()[0] == (
-                len(self.layers)
-            ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
-
-        for idx, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_states,)
-            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
-            to_drop = False
-            if self.training:
-                dropout_probability = torch.rand([])
-                if dropout_probability < self.layerdrop:  # skip the layer
-                    to_drop = True
-
-            if to_drop:
-                layer_outputs = (None, None)
-            else:
-                if self.gradient_checkpointing and self.training:
-                    layer_outputs = self._gradient_checkpointing_func(
-                        encoder_layer.__call__,
-                        hidden_states,
-                        None,
-                        (head_mask[idx] if head_mask is not None else None),
-                        output_attentions,
-                    )
-                else:
-                    layer_outputs = encoder_layer(
-                        hidden_states,
-                        None,
-                        layer_head_mask=(head_mask[idx] if head_mask is not None else None),
-                        output_attentions=output_attentions,
-                    )
-
-                hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        hidden_states = self.layer_norm(hidden_states)
-        if output_hidden_states:
-            encoder_states = encoder_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
-        )
-
-
-# copied from Qwen2AudioCausalLMOutputWithPast
-@dataclass
-class MERaLiONOutputWithPast(ModelOutput):
-    """
-    Base class for MERaLiON 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.
-        attention_mask (`torch.FloatTensor`, *optional*):
-            Attentions mask, used to update attention mask and position_ids.
-    """
-
-    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
-    attention_mask: Optional[torch.FloatTensor] = None
-
-
-MERALION_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 ([`MERaLiONConfig`]):
-            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 MERaLiON Model outputting raw hidden-states without any specific head on top.",
-    MERALION_START_DOCSTRING,
-)
-class MERaLiONPreTrainedModel(PreTrainedModel):
-    config_class = MERaLiONConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["MERaLiONSpeechEncoderLayer", "MERaLiONSpeechDecoderLayer", "MERaLiONTextDecoderLayer"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-    _supports_static_cache = True
-
-    def _init_weights(self, module):
-        # important: this ported version of Qwen2Audio isn't meant for training from scratch - only
-        # inference and fine-tuning - so the proper init weights code has been removed
-        std = self.config.init_std if hasattr(self.config, "init_std") else self.config.speech_config.init_std
-
-        if isinstance(module, (nn.Linear, nn.Conv1d)):
-            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.text_decoder._supports_sdpa
-
-class MERaLiONSpeechAudioAdaper(nn.Module):
-    def __init__(
-        self,
-        config,
-        **kwargs
-    ):
-        super(MERaLiONSpeechAudioAdaper, self).__init__()
-        speech_audio_encoder_output_dim = config.speech_config.d_model
-        llm_input_hidden_size = config.text_config.hidden_size
-        speech_mlp_scale_factor = config.speech_mlp_scale_factor
-
-        self.speech_mlp_scale_factor = speech_mlp_scale_factor
-        self.mlp_adapter = nn.Sequential(
-            nn.Linear(
-                in_features=speech_audio_encoder_output_dim * speech_mlp_scale_factor,
-                out_features=speech_audio_encoder_output_dim
-            ),
-            nn.SiLU(),
-            nn.Dropout(0.1),
-        )
-
-        self.speech_llm_proj = nn.Sequential(
-                nn.Linear(
-                    speech_audio_encoder_output_dim,
-                    speech_audio_encoder_output_dim * 4
-                ),
-                nn.SiLU(),
-                nn.Dropout(0.1),
-
-                nn.Linear(
-                    speech_audio_encoder_output_dim * 4,
-                    llm_input_hidden_size
-                ),
-            )
-
-    def forward(self, speech_embeds, **kwargs):
-        B, T, C = speech_embeds.shape
-        speech_embeds = self.mlp_adapter(
-            speech_embeds.reshape(
-                B,
-                T // self.speech_mlp_scale_factor,
-                C * self.speech_mlp_scale_factor,
-            )
-        )
-        return self.speech_llm_proj(speech_embeds)
-
-
-MERALION_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)
-        input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, feature_sequence_length)`, *optional*):
-            Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
-            loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
-            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
-            [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
-            tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
-        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**.
-        feature_attention_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **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.
-"""
-
-@add_start_docstrings(
-    """The MERALION model which consists of a audio backbone and a language model.""",
-    MERALION_START_DOCSTRING,
-)
-class MERaLiONForConditionalGeneration(MERaLiONPreTrainedModel, GenerationMixin):
-    def __init__(self, config: MERaLiONConfig):
-        config.text_config._attn_implementation = config._attn_implementation
-        config.speech_config._attn_implementation = config._attn_implementation
-
-        super().__init__(config)
-
-        self.speech_encoder = MERaLiONSpeechEncoder(config.speech_config)
-        # self.speech_encoder = AutoModel.from_config(config.audio_config, attn_implementation=config._attn_implementation)
-
-        self.ln_speech = nn.LayerNorm(config.speech_config.d_model)
-        self.speech_audio_adapter = MERaLiONSpeechAudioAdaper(config)
-        self.vocab_size = config.text_config.vocab_size
-        self.text_decoder = MERaLiONTextForCausalLM(config.text_config)
-        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
-
-    # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_input_embeddings
-    def get_input_embeddings(self):
-        return self.text_decoder.get_input_embeddings()
-
-    # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_input_embeddings
-    def set_input_embeddings(self, value):
-        self.text_decoder.set_input_embeddings(value)
-
-    # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_output_embeddings
-    def get_output_embeddings(self):
-        return self.text_decoder.get_output_embeddings()
-
-    # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_output_embeddings
-    def set_output_embeddings(self, new_embeddings):
-        self.text_decoder.set_output_embeddings(new_embeddings)
-
-    # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_decoder
-    def set_decoder(self, decoder):
-        self.text_decoder.set_decoder(decoder)
-
-    # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_decoder
-    def get_decoder(self):
-        return self.text_decoder.get_decoder()
-
-    # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.tie_weights
-    def tie_weights(self):
-        return self.text_decoder.tie_weights()
-
-    # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.resize_token_embeddings
-    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
-        model_embeds = self.text_decoder.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
-
-    @add_start_docstrings_to_model_forward(MERALION_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=MERaLiONOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        input_features: torch.FloatTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        feature_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,
-        cache_position: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, MERaLiONOutputWithPast]:
-        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:
-        """
-
-        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
-
-        speech_encoder_device = self.speech_encoder.device
-
-        if input_features is not None:
-            input_features = input_features.to(speech_encoder_device)
-            feature_attention_mask = feature_attention_mask.to(speech_encoder_device)
-
-            if inputs_embeds is None:
-                speech_contexts_embeds = self.speech_encoder(input_features, attention_mask=feature_attention_mask).last_hidden_state
-                speech_contexts_embeds = self.ln_speech(speech_contexts_embeds)
-                speech_audio_contexts_embeds = self.speech_audio_adapter(speech_contexts_embeds)
-
-                inputs_embeds = self.text_decoder.base_model.embed_tokens(input_ids)
-
-                speech_mask = (input_ids == self.config.speech_token_index).unsqueeze(-1)
-                speech_mask = speech_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
-
-                inputs_embeds = inputs_embeds.masked_scatter(speech_mask, speech_audio_contexts_embeds)
-
-                input_ids = None
-
-        outputs = self.text_decoder(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            cache_position=cache_position,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            labels=labels
-        )
-
-        return outputs
-
-    # from transformers.models.gemma2.modeling_gemma2.Gemma2ForCausalLM.prepare_inputs_for_generation
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        attention_mask=None,
-        input_features=None,
-        feature_attention_mask=None,
-        past_key_values=None,
-        inputs_embeds=None,
-        cache_position=None,
-        position_ids=None,
-        use_cache=None,
-        **kwargs,
-    ):
-        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
-        # Exception 1: when passing input_embeds, input_ids may be missing entries
-        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
-        is_first_step = cache_position[0].item() == 0
-        if past_key_values is not None:
-            if inputs_embeds is not None:  # Exception 1
-                input_ids = input_ids[:, -cache_position.shape[0] :]
-            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
-                input_ids = input_ids[:, cache_position]
-
-        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] :]
-                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s
-                # `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride
-                # during the decoding. Here, simply using `.contiguous()` is not sufficient as in the
-                # batch size = 1 case, `position_ids` is already contiguous but with varying stride
-                # which retriggers a capture.
-                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and is_first_step:
-            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
-        else:
-            # The clone here is for the same reason as for `position_ids`.
-            model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
-
-        if (
-            isinstance(past_key_values, HybridCache)
-            and attention_mask.ndim == 2
-            and not self.config._attn_implementation == "flash_attention_2"
-        ):
-            if model_inputs["inputs_embeds"] is not None:
-                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
-                device = model_inputs["inputs_embeds"].device
-            else:
-                batch_size, sequence_length = model_inputs["input_ids"].shape
-                device = model_inputs["input_ids"].device
-            dtype = self.text_decoder.lm_head.weight.dtype
-            min_dtype = torch.finfo(dtype).min
-            attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
-                attention_mask,
-                sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
-                dtype=dtype,
-                device=device,
-                min_dtype=min_dtype,
-                cache_position=cache_position,
-                batch_size=batch_size,
-            )
-
-        model_inputs.update(
-            {
-                "attention_mask": attention_mask,
-                "position_ids": position_ids,
-                "cache_position": cache_position,
-                "past_key_values": past_key_values,
-                "use_cache": use_cache
-            }
-        )
-
-        # Input ids will only be used from the second step. 
-        if is_first_step:
-            model_inputs["input_features"] = input_features
-            model_inputs["feature_attention_mask"] = feature_attention_mask
-
-        return model_inputs
-
-    def _reorder_cache(self, *args, **kwargs):
-        return self.text_decoder._reorder_cache(*args, **kwargs)

vllm_plugin_meralion/vllm_plugin_meralion/modeling_text_decoder.py

@@ -1,1319 +0,0 @@
-"""PyTorch MERaLiON AudioLLM model text decoder."""
-
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, HybridCache
-from transformers.generation import GenerationMixin
-from transformers.modeling_flash_attention_utils import _flash_attention_forward
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_flash_attn_greater_or_equal,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_meralion import MERaLiONTextConfig
-
-
-_CHECKPOINT_FOR_DOC = "MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION"
-
-
-class MERaLiONTextRMSNorm(nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-6):
-        super().__init__()
-        self.eps = eps
-        self.weight = nn.Parameter(torch.zeros(dim))
-
-    def _norm(self, x):
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-
-    def forward(self, x):
-        output = self._norm(x.float())
-        # Llama does x.to(float16) * w whilst MERaLiONText is (x * w).to(float16)
-        # See https://github.com/huggingface/transformers/pull/29402
-        output = output * (1.0 + self.weight.float())
-        return output.type_as(x)
-
-    def extra_repr(self):
-        return f"{tuple(self.weight.shape)}, eps={self.eps}"
-
-
-class MERaLiONTextMLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        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_activation]
-
-    def forward(self, x):
-        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-
-logger = logging.get_logger(__name__)
-
-
-class MERaLiONTextRotaryEmbedding(nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
-        super().__init__()
-
-        self.dim = dim
-        self.max_position_embeddings = max_position_embeddings
-        self.base = base
-        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
-        self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)
-
-    @torch.no_grad()
-    def forward(self, x, position_ids, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        self.inv_freq.to(x.device)
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-        # Force float32 since bfloat16 loses precision on long contexts
-        # 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(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos()
-            sin = emb.sin()
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-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(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    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`, *optional*):
-            Deprecated and unused.
-        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.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.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 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 MERaLiONTextAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: MERaLiONTextConfig, 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 a `layer_idx` is not recommended and will "
-                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
-        self.attention_dropout = config.attention_dropout
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = config.head_dim
-        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 = True
-        self.scaling = config.query_pre_attn_scalar**-0.5
-
-        if self.hidden_size % self.num_heads != 0:
-            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=config.attention_bias)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
-        self.sliding_window = config.sliding_window if not bool(layer_idx % 2) else None
-        self.rotary_emb = MERaLiONTextRotaryEmbedding(
-            self.head_dim,
-            max_position_embeddings=self.max_position_embeddings,
-            base=self.rope_theta,
-        )
-
-    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,
-    ) -> 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)
-
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {
-                "sin": sin,
-                "cos": cos,
-                "sliding_window": self.sliding_window,
-                "cache_position": cache_position,
-            }
-            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)
-
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
-
-        if self.config.attn_logit_softcapping is not None:
-            attn_weights = attn_weights / self.config.attn_logit_softcapping
-            attn_weights = torch.tanh(attn_weights)
-            attn_weights = attn_weights * self.config.attn_logit_softcapping
-        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
-
-        # 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.view(bsz, q_len, -1)
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class MERaLiONTextFlashAttention2(MERaLiONTextAttention):
-    """
-    MERaLiONText flash attention module. This module inherits from `MERaLiONTextAttention` as the weights of the module stays
-    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
-    flash attention and deal with padding tokens in case the input contains any of them.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.LongTensor] = 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,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        output_attentions = False
-
-        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)
-
-        # Flash attention requires the input to have the shape
-        # batch_size x seq_length x head_dim x hidden_dim
-        # therefore we just need to keep the original shape
-        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)
-
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {
-                "sin": sin,
-                "cos": cos,
-                "sliding_window": self.sliding_window,
-                "cache_position": cache_position,
-            }
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        if attention_mask is not None:
-            seq_len = attention_mask.shape[1]
-            key_states = key_states[:, :, :seq_len]
-            value_states = value_states[:, :, :seq_len]
-
-        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
-        # to be able to avoid many of these transpose/reshape/view.
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        dropout_rate = self.attention_dropout if self.training else 0.0
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in the correct dtype just to be sure everything works as expected.
-        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
-        # in fp32. (MERaLiONTextRMSNorm handles it correctly)
-
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.q_proj.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        attn_output = _flash_attention_forward(
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            q_len,
-            dropout=dropout_rate,
-            softmax_scale=self.scaling,
-            is_causal=self.is_causal,
-            sliding_window=self.sliding_window,
-            use_top_left_mask=self._flash_attn_uses_top_left_mask,
-            softcap=self.config.attn_logit_softcapping if is_flash_attn_greater_or_equal("2.6.0") else None,
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class MERaLiONTextSdpaAttention(MERaLiONTextAttention):
-    """
-    MERaLiONText attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
-    `MERaLiONTextAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
-    SDPA API.
-    """
-
-    # Adapted from MERaLiONTextAttention.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,
-    ) -> 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.
-            logger.warning_once(
-                "MERaLiONTextModel is using MERaLiONTextSdpaAttention, 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,
-            )
-
-        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)
-
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {
-                "sin": sin,
-                "cos": cos,
-                "sliding_window": self.sliding_window,
-                "cache_position": cache_position,
-            }
-            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)
-
-        causal_mask = attention_mask
-        if attention_mask is not None:
-            causal_mask = causal_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 causal_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.
-        is_causal = True if causal_mask is None and q_len > 1 else False
-
-        attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_states,
-            key_states,
-            value_states,
-            attn_mask=causal_mask,
-            dropout_p=self.attention_dropout if self.training else 0.0,
-            is_causal=is_causal,
-            scale=self.scaling,
-        )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, -1)
-
-        attn_output = self.o_proj(attn_output)
-
-        return attn_output, None, past_key_value
-
-
-MERALION_TEXT_ATTENTION_CLASSES = {
-    "eager": MERaLiONTextAttention,
-    "flash_attention_2": MERaLiONTextFlashAttention2,
-    "sdpa": MERaLiONTextSdpaAttention,
-}
-
-
-class MERaLiONTextDecoderLayer(nn.Module):
-    def __init__(self, config: MERaLiONTextConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = MERALION_TEXT_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
-        self.mlp = MERaLiONTextMLP(config)
-        self.input_layernorm = MERaLiONTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.config = config
-        self.is_sliding = not bool(layer_idx % 2)
-        self.pre_feedforward_layernorm = MERaLiONTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_feedforward_layernorm = MERaLiONTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.sliding_window = config.sliding_window
-        self.post_attention_layernorm = MERaLiONTextRMSNorm(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[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> 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_size, sequence_length)` if flash attention is used or `(batch_size, 1,
-                query_sequence_length, key_sequence_length)` if default attention is used.
-            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
-            kwargs (`dict`, *optional*):
-                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
-                into the model
-        """
-        if self.is_sliding and attention_mask is not None:  # efficient SDPA and no padding
-            # Flash-attn is a 2D tensor
-            if self.config._attn_implementation == "flash_attention_2":
-                if past_key_value is not None:  # when decoding
-                    attention_mask = attention_mask[:, -self.sliding_window :]
-            else:
-                min_dtype = torch.finfo(hidden_states.dtype).min
-                sliding_window_mask = torch.tril(
-                    torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window
-                )
-                attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
-                if attention_mask.shape[-1] <= 1:  # when decoding
-                    attention_mask = attention_mask[:, :, :, -self.sliding_window :]
-
-        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,
-        )
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.pre_feedforward_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = self.post_feedforward_layernorm(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
-
-
-MERALION_TEXT_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 ([`MERaLiONTextConfig`]):
-            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 MERaLiONText Model outputting raw hidden-states without any specific head on top.",
-    MERALION_TEXT_START_DOCSTRING,
-)
-class MERaLiONTextPreTrainedModel(PreTrainedModel):
-    config_class = MERaLiONTextConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["MERaLiONTextDecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-    _supports_quantized_cache = False
-    _supports_static_cache = True
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            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 _check_and_enable_sdpa(cls, config, hard_check_only: bool = False):
-        """
-        Overloads `PreTrainedModel._check_and_enable_sdpa` so as to DISABLE torch SDPA by default on MERaLiONText models.
-        SDPA reduces the model performance on MERaLiONText because of the logits softcapping.
-        """
-        config = super()._check_and_enable_sdpa(config, hard_check_only=hard_check_only)
-
-        # if using the default path -> swap sdpa by eager
-        if not hard_check_only and config._attn_implementation == "sdpa":
-            config._attn_implementation = "eager"
-
-        return config
-
-
-_CONFIG_FOR_DOC = "MERaLiONTextConfig"
-
-
-MERALION_TEXT_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 `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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
-            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
-            Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance, see our
-            [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
-            - 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)`). This is also known as the legacy
-            cache format.
-
-            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
-            legacy cache format will be returned.
-
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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.
-        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
-            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
-            the complete sequence length.
-"""
-
-
-@add_start_docstrings(
-    "The bare MERaLiONText Model outputting raw hidden-states without any specific head on top.",
-    MERALION_TEXT_START_DOCSTRING,
-)
-class MERaLiONTextModel(MERaLiONTextPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MERaLiONTextDecoderLayer`]
-
-    Args:
-        config: MERaLiONTextConfig
-    """
-
-    def __init__(self, config: MERaLiONTextConfig):
-        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(
-            [MERaLiONTextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = MERaLiONTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        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
-
-    @add_start_docstrings_to_model_forward(MERALION_TEXT_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[HybridCache] = 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 and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        if use_cache and past_key_values is None and not self.training:
-            batch_size, seq_len, _ = inputs_embeds.shape
-            past_key_values = HybridCache(
-                self.config,
-                batch_size=batch_size,
-                max_cache_len=seq_len,
-                device=self.device,
-                dtype=inputs_embeds.dtype,
-            )
-
-        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
-            )
-
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = self._update_causal_mask(
-            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-        )
-
-        # embed positions
-        hidden_states = inputs_embeds
-
-        # normalized
-        # MERaLiONText downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
-        # See https://github.com/huggingface/transformers/pull/29402
-        normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
-        hidden_states = hidden_states * normalizer
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-
-        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,
-                    causal_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                    cache_position,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-        hidden_states = self.norm(hidden_states)
-
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        next_cache = past_key_values if use_cache else None
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, 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,
-        )
-
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_key_values: HybridCache,
-        output_attentions: bool,
-    ):
-        # Flash Attention currently doesn't support static cache but MERaLiONText work only with static cache.
-        # So we will pass in attention mask as is in any case, not only when ther's padding. Then we'll use its shape
-        # to cut out keys/values trailing 0 used in static cache. This workaround should be compile compatible
-        # as it doesn't cause dynamic control issues.
-        if self.config._attn_implementation == "flash_attention_2":
-            return attention_mask
-
-        dtype, device = input_tensor.dtype, input_tensor.device
-        sequence_length = input_tensor.shape[1]
-        if isinstance(past_key_values, HybridCache):
-            target_length = past_key_values.get_max_cache_shape()
-        else:
-            target_length = attention_mask.shape[-1] if attention_mask is not None else input_tensor.shape[1]
-
-        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
-        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
-            attention_mask,
-            sequence_length=sequence_length,
-            target_length=target_length,
-            dtype=dtype,
-            device=device,
-            cache_position=cache_position,
-            batch_size=input_tensor.shape[0],
-        )
-        return causal_mask
-
-    @staticmethod
-    def _prepare_4d_causal_attention_mask_with_cache_position(
-        attention_mask: torch.Tensor,
-        sequence_length: int,
-        target_length: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        cache_position: torch.Tensor,
-        batch_size: int,
-        **kwargs,
-    ):
-        """
-        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
-        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
-
-        Args:
-            attention_mask (`torch.Tensor`):
-                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
-                `(batch_size, 1, query_length, key_value_length)`.
-            sequence_length (`int`):
-                The sequence length being processed.
-            target_length (`int`):
-                The target length: when generating with static cache, the mask should be as long as the static cache,
-                to account for the 0 padding, the part of the cache that is not filled yet.
-            dtype (`torch.dtype`):
-                The dtype to use for the 4D attention mask.
-            device (`torch.device`):
-                The device to plcae the 4D attention mask on.
-            cache_position (`torch.Tensor`):
-                Indices depicting the position of the input sequence tokens in the sequence.
-            batch_size (`torch.Tensor`):
-                Batch size.
-        """
-        if attention_mask is not None and attention_mask.dim() == 4:
-            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
-            causal_mask = attention_mask
-        else:
-            min_dtype = torch.finfo(dtype).min
-            causal_mask = torch.full(
-                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
-            )
-            if sequence_length != 1:
-                causal_mask = torch.triu(causal_mask, diagonal=1)
-            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
-            if attention_mask is not None:
-                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-                mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
-                padding_mask = padding_mask == 0
-                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                    padding_mask, min_dtype
-                )
-
-        return causal_mask
-
-
-class MERaLiONTextForCausalLM(MERaLiONTextPreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = MERaLiONTextModel(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    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
-
-    @add_start_docstrings_to_model_forward(MERALION_TEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutputWithPast, 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[HybridCache] = 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,
-        cache_position: Optional[torch.LongTensor] = None,
-        num_logits_to_keep: int = 0,
-        **loss_kwargs,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        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]`.
-
-            num_logits_to_keep (`int`, *optional*):
-                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
-                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-
-        Returns:
-        """
-
-        if self.training and self.config._attn_implementation != "eager":
-            logger.warning_once(
-                "It is strongly recommended to train MERaLiONText models with the `eager` attention implementation "
-                f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
-            )
-        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
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            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]
-        # 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:, :])
-        if self.config.final_logit_softcapping is not None:
-            logits = logits / self.config.final_logit_softcapping
-            logits = torch.tanh(logits)
-            logits = logits * self.config.final_logit_softcapping
-
-        loss = None
-        if labels is not None:
-            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 CausalLMOutputWithPast(
-            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,
-        attention_mask=None,
-        inputs_embeds=None,
-        cache_position=None,
-        position_ids=None,
-        use_cache=True,
-        num_logits_to_keep=None,
-        **kwargs,
-    ):
-        # Overwritten: has a special cache type, `HybridCache`
-
-        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
-        # Exception 1: when passing input_embeds, input_ids may be missing entries
-        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
-        if past_key_values is not None:
-            if inputs_embeds is not None:  # Exception 1
-                input_ids = input_ids[:, -cache_position.shape[0] :]
-            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
-                input_ids = input_ids[:, cache_position]
-        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] :]
-                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s
-                # `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride
-                # during the decoding. Here, simply using `.contiguous()` is not sufficient as in the
-                # batch size = 1 case, `position_ids` is already contiguous but with varying stride
-                # which retriggers a capture.
-                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and cache_position[0] == 0:
-            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
-        else:
-            # The clone here is for the same reason as for `position_ids`.
-            model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
-
-        if (
-            isinstance(past_key_values, HybridCache)
-            and attention_mask.ndim == 2
-            and not self.config._attn_implementation == "flash_attention_2"
-        ):
-            if model_inputs["inputs_embeds"] is not None:
-                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
-                device = model_inputs["inputs_embeds"].device
-            else:
-                batch_size, sequence_length = model_inputs["input_ids"].shape
-                device = model_inputs["input_ids"].device
-
-            attention_mask = self.model._prepare_4d_causal_attention_mask_with_cache_position(
-                attention_mask,
-                sequence_length=sequence_length,
-                target_length=past_key_values.get_max_cache_shape(),
-                dtype=self.lm_head.weight.dtype,
-                device=device,
-                cache_position=cache_position,
-                batch_size=batch_size,
-            )
-
-        if num_logits_to_keep is not None:
-            model_inputs["num_logits_to_keep"] = num_logits_to_keep
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids,
-                "cache_position": cache_position,
-                "past_key_values": past_key_values,
-                "use_cache": use_cache,
-                "attention_mask": attention_mask,
-            }
-        )
-        return model_inputs
-
-
-@add_start_docstrings(
-    """
-    The MERaLiONText Model transformer with a sequence classification head on top (linear layer).
-
-    [`MERaLiONTextForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    MERALION_TEXT_START_DOCSTRING,
-)
-class MERaLiONTextForSequenceClassification(MERaLiONTextPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = MERaLiONTextModel(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(MERALION_TEXT_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Union[Cache, 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,
-    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.model(
-            input_ids,
-            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,
-        )
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.pad_token_id is None:
-            sequence_lengths = -1
-        else:
-            if input_ids is not None:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.to(logits.device)
-            else:
-                sequence_lengths = -1
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
-
-        if not return_dict:
-            output = (pooled_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    The MERaLiONText Model transformer with a token classification head on top (a linear layer on top of the hidden-states
-    output) e.g. for Named-Entity-Recognition (NER) tasks.
-    """,
-    MERALION_TEXT_START_DOCSTRING,
-)
-class MERaLiONTextForTokenClassification(MERaLiONTextPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = MERaLiONTextModel(config)
-        if getattr(config, "classifier_dropout", None) is not None:
-            classifier_dropout = config.classifier_dropout
-        elif getattr(config, "hidden_dropout", None) is not None:
-            classifier_dropout = config.hidden_dropout
-        else:
-            classifier_dropout = 0.1
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.score = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(MERALION_TEXT_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: 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,
-        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, TokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.model(
-            input_ids,
-            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,
-        )
-        sequence_output = outputs[0]
-        sequence_output = self.dropout(sequence_output)
-        logits = self.score(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.config)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )

vllm_plugin_meralion/vllm_plugin_meralion/vllm_meralion.py

@@ -13,26 +13,30 @@ from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
                          InputContext, token_inputs)
 from vllm.logger import init_logger
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.pooler import Pooler, PoolingType
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
 from vllm.model_executor.models.gemma2 import Gemma2Model
+from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
 from vllm.multimodal.utils import consecutive_placeholder_ranges
-from vllm.sequence import IntermediateTensors, SequenceData
+from vllm.sequence import IntermediateTensors, SequenceData, PoolerOutput
+from transformers.models.whisper.modeling_whisper import WhisperEncoder
+from transformers.utils.import_utils import is_torch_sdpa_available, is_flash_attn_2_available
 
-from vllm.model_executor.models.interfaces import SupportsMultiModal, SupportsPP
+from vllm.model_executor.models.interfaces import SupportsMultiModal, SupportsLoRA, SupportsPP
 from vllm.model_executor.models.utils import maybe_prefix
 
-from .modeling_meralion import MERaLiONSpeechEncoder
 
 logger = init_logger(__name__)
 
+
 # gemma2 ties word embedding by default
 _KEYS_TO_MODIFY_MAPPING = {
-    "text_decoder.model": "text_decoder",
+    "text_decoder.model": "model",
 }
 
 # === Constants === #
@@ -96,6 +100,17 @@ class MERaLiONSpeechAudioAdaper(nn.Module):
         return self.speech_llm_proj(speech_embeds)
 
 
+def _autoset_attn_implementation_for_whisper(config):
+    _implementation = "eager"
+    if is_torch_sdpa_available():
+        _implementation = "sdpa"
+    if is_flash_attn_2_available():
+        _implementation = "flash_attention_2"
+
+    config._attn_implementation = _implementation
+    return config
+
+
 def dummy_data_for_meralion(ctx: InputContext, seq_len: int,
                                mm_counts: Mapping[str, int]):
     num_audios = mm_counts["audio"]
@@ -180,11 +195,22 @@ def _get_chunked_audios(audios: List[np.ndarray]):
     for audio_idx, audio in enumerate(audios):
         for cid in range(audio_number_chunks[audio_idx]):
             chunked_resampled_audios.append(
-                audio[cid * FEATURE_CHUNK_SIZE: (cid + 1) * FEATURE_CHUNK_SIZE].copy()
+                audio[cid * FEATURE_CHUNK_SIZE: (cid + 1) * FEATURE_CHUNK_SIZE]
             )
     return chunked_resampled_audios
 
 
+def _maybe_resample_audio(audio, orig_sample_rate, target_sample_rate):
+    if orig_sample_rate != target_sample_rate:
+        return librosa.resample(
+            audio,
+            orig_sr=orig_sample_rate,
+            target_sr=target_sample_rate
+            )
+    
+    return audio
+
+
 def get_max_meralion_audio_tokens(ctx: InputContext) -> int:
     """
     The max number of tokens after speech audio adapter.
@@ -206,6 +232,7 @@ def input_processor_for_meralion(
         return inputs
 
     processor = cached_get_processor(ctx.model_config.model)
+
     resampled_audios = [
         librosa.resample(audio,
                          orig_sr=sampling_rate,
@@ -259,18 +286,21 @@ def input_mapper_for_meralion(
             "to process the audio object")
 
     try:
+        target_sample_rate = processor.feature_extractor.sampling_rate
+
         resampled_audios = [
-            librosa.resample(
-                audio,
-                orig_sr=sampling_rate,
-                target_sr=processor.feature_extractor.sampling_rate)
+            _maybe_resample_audio(
+                audio=audio,
+                orig_sample_rate=sampling_rate, 
+                target_sample_rate=target_sample_rate, 
+                )
             for audio, sampling_rate in multi_modal_data
         ]
 
         resampled_audios = _get_chunked_audios(resampled_audios)
 
         batch_data = audio_feature_extractor(resampled_audios,
-                                             sampling_rate=16000,
+                                             sampling_rate=target_sample_rate,
                                              return_attention_mask=True,
                                              padding="max_length",
                                              return_tensors="pt").data
@@ -289,29 +319,57 @@ def input_mapper_for_meralion(
 @MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
     "audio", get_max_meralion_audio_tokens)
 class MERaLiONForConditionalGeneration(nn.Module, SupportsMultiModal,
-                                         SupportsPP):
+                                       SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+    ]
+
+    embedding_modules = {}
+    embedding_padding_modules = []
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config
+        pooler_config = vllm_config.model_config.pooler_config
+        # lora_config = vllm_config.lora_config
+
         self.config = config
         self.multimodal_config = multimodal_config
+        # self.lora_config = lora_config
 
-        self.speech_encoder = MERaLiONSpeechEncoder(config.speech_config)
+        config.speech_config = \
+            _autoset_attn_implementation_for_whisper(config.speech_config)
+        self.speech_encoder = WhisperEncoder(config.speech_config)
         self.ln_speech = nn.LayerNorm(config.speech_config.d_model)
         self.speech_audio_adapter = MERaLiONSpeechAudioAdaper(
             config.speech_config.d_model, config.text_config.hidden_size)
 
         self.quant_config = quant_config
 
-        self.text_decoder = Gemma2Model(
+        self.model = Gemma2Model(
             vllm_config=vllm_config.with_hf_config(config.text_config),
             prefix=maybe_prefix(prefix, "model"))
         self.unpadded_vocab_size = config.text_config.vocab_size
         if config.text_config.tie_word_embeddings:
-            self.lm_head = self.text_decoder.embed_tokens
+            self.lm_head = self.model.embed_tokens
         else:
             self.lm_head = ParallelLMHead(config.text_config.vocab_size,
                                           config.text_config.hidden_size,
@@ -323,8 +381,16 @@ class MERaLiONForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         self.sampler = get_sampler()
 
+        # The same model class supports both language generation and embedding
+        # because the architecture name is the same
+        self._pooler = Pooler.from_config_with_defaults(
+            pooler_config,
+            pooling_type=PoolingType.LAST,
+            normalize=True,
+            softmax=False)
+
         self.make_empty_intermediate_tensors = (
-            self.text_decoder.make_empty_intermediate_tensors)
+            self.model.make_empty_intermediate_tensors)
 
     def _validate_and_reshape_mm_tensor(self,
                                         mm_input: Union[torch.Tensor,
@@ -387,7 +453,7 @@ class MERaLiONForConditionalGeneration(nn.Module, SupportsMultiModal,
             if audio_input is None:
                 inputs_embeds = None
             else:
-                inputs_embeds = self.text_decoder.embed_tokens(input_ids)
+                inputs_embeds = self.model.embed_tokens(input_ids)
                 processed_audio_features = self._process_audio_input(audio_input)
                 # merge llm embeddings and audio features
                 mask = (input_ids == self.config.speech_token_index)
@@ -395,7 +461,7 @@ class MERaLiONForConditionalGeneration(nn.Module, SupportsMultiModal,
 
                 input_ids = None
 
-        hidden_states = self.text_decoder(
+        hidden_states = self.model(
             input_ids=input_ids,
             positions=positions,
             kv_caches=kv_caches,
@@ -418,6 +484,13 @@ class MERaLiONForConditionalGeneration(nn.Module, SupportsMultiModal,
     ) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(logits, sampling_metadata)
         return next_tokens
+    
+    def pooler(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Optional[PoolerOutput]:
+        return self._pooler(hidden_states, pooling_metadata)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [
@@ -462,4 +535,4 @@ class MERaLiONForConditionalGeneration(nn.Module, SupportsMultiModal,
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)
-                weight_loader(param, loaded_weight)
+                weight_loader(param, loaded_weight)