modeling_meralion.py

"""PyTorch MERaLiON AudioLLM model."""

from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn

from transformers import Gemma2ForCausalLM
from transformers.models.whisper.modeling_whisper import WhisperEncoder
from transformers.cache_utils import HybridCache
from transformers.generation import GenerationMixin
from transformers.modeling_outputs import ModelOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)

from .configuration_meralion import MERaLiONConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "MERaLiONConfig"


# 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


# 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 = ["WhisperEncoderLayer", "WhisperDecoderLayer", "Gemma2DecoderLayer"]
    _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 = WhisperEncoder(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 = Gemma2ForCausalLM(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)