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"""PyTorch MERaLiON2 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_meralion2 import MERaLiON2Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MERaLiON2Config"
# 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 MERaLiON2OutputWithPast(ModelOutput):
"""
Base class for MERaLiON2 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 ([`MERaLiON2Config`]):
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 MERaLiON2 Model outputting raw hidden-states without any specific head on top.",
MERALION_START_DOCSTRING,
)
class MERaLiON2PreTrainedModel(PreTrainedModel):
config_class = MERaLiON2Config
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 MERaLiON2SpeechAudioAdaper(nn.Module):
def __init__(
self,
config,
**kwargs
):
super(MERaLiON2SpeechAudioAdaper, 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)
class MERaLiON2SpeechAudioAdaperLarge(nn.Module):
def __init__(
self,
config,
**kwargs
):
super(MERaLiON2SpeechAudioAdaperLarge, 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 * 5,
),
nn.SiLU(),
nn.Dropout(0.01),
)
self.gate_proj = nn.Linear(
in_features=speech_audio_encoder_output_dim * 5,
out_features=speech_audio_encoder_output_dim * 5,
)
self.pool_proj = nn.Linear(
in_features=speech_audio_encoder_output_dim * 5,
out_features=speech_audio_encoder_output_dim * 5,
)
self.act_fn = nn.SiLU()
self.out_proj = nn.Linear(
speech_audio_encoder_output_dim * 5,
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,
)
)
speech_embeds = self.act_fn(self.gate_proj(speech_embeds)) * self.pool_proj(speech_embeds)
speech_embeds = self.out_proj(speech_embeds)
return 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 MERaLiON2ForConditionalGeneration(MERaLiON2PreTrainedModel, GenerationMixin):
def __init__(self, config: MERaLiON2Config):
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 = MERaLiON2SpeechAudioAdaperLarge(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=MERaLiON2OutputWithPast, 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, MERaLiON2OutputWithPast]:
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_cache_shape(),
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) |