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from typing import Union, Type |
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import torch |
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from transformers.modeling_outputs import SequenceClassifierOutput |
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from transformers import ( |
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PreTrainedModel, |
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PretrainedConfig, |
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WavLMConfig, |
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BertConfig, |
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WavLMModel, |
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BertModel, |
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Wav2Vec2Config, |
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Wav2Vec2Model |
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) |
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from transformers.models.wavlm.modeling_wavlm import ( |
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WavLMEncoder, |
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WavLMEncoderStableLayerNorm, |
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WavLMFeatureEncoder |
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) |
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from transformers.models.bert.modeling_bert import BertEncoder |
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class MultiModalConfig(PretrainedConfig): |
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"""Base class for multimodal configs""" |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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class WavLMBertConfig(MultiModalConfig): |
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... |
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class BaseClassificationModel(PreTrainedModel): |
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config: Type[Union[PretrainedConfig, None]] = None |
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def compute_loss(self, logits, labels): |
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"""Compute loss |
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Args: |
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logits (torch.FloatTensor): logits |
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labels (torch.LongTensor): labels |
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Returns: |
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torch.FloatTensor: loss |
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Raises: |
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ValueError: Invalid number of labels |
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""" |
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if self.config.problem_type is None: |
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if self.num_labels == 1: |
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self.config.problem_type = "regression" |
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elif self.num_labels > 1: |
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self.config.problem_type = "single_label_classification" |
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else: |
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raise ValueError("Invalid number of labels: {}".format(self.num_labels)) |
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if self.config.problem_type == "single_label_classification": |
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loss_fct = torch.nn.CrossEntropyLoss() |
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) |
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elif self.config.problem_type == "multi_label_classification": |
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loss_fct = torch.nn.BCEWithLogitsLoss(weight=torch.tensor([1.4411, 2.1129, 0.9927, 1.6995, 0.9038, 0.4126, 1.4150]).to("cuda")) |
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1, self.num_labels)) |
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elif self.config.problem_type == "regression": |
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loss_fct = torch.nn.MSELoss() |
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loss = loss_fct(logits.view(-1), labels.view(-1)) |
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else: |
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raise ValueError("Problem_type {} not supported".format(self.config.problem_type)) |
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return loss |
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@staticmethod |
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def merged_strategy( |
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hidden_states, |
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mode="mean" |
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): |
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"""Merged strategy for pooling |
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Args: |
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hidden_states (torch.FloatTensor): hidden states |
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mode (str, optional): pooling mode. Defaults to "mean". |
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Returns: |
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torch.FloatTensor: pooled hidden states |
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""" |
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if mode == "mean": |
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outputs = torch.mean(hidden_states, dim=1) |
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elif mode == "sum": |
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outputs = torch.sum(hidden_states, dim=1) |
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elif mode == "max": |
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outputs = torch.max(hidden_states, dim=1)[0] |
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else: |
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raise Exception( |
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"The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']") |
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return outputs |
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class AudioTextModelForSequenceBaseClassification(BaseClassificationModel): |
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config_class = WavLMBertConfig |
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def __init__(self, config): |
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""" |
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Args: |
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config (MultiModalConfig): config |
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Attributes: |
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config (MultiModalConfig): config |
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num_labels (int): number of labels |
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audio_config (Union[PretrainedConfig, None]): audio config |
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text_config (Union[PretrainedConfig, None]): text config |
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audio_model (Union[PreTrainedModel, None]): audio model |
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text_model (Union[PreTrainedModel, None]): text model |
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classifier (Union[torch.nn.Linear, None]): classifier |
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""" |
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super().__init__(config) |
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self.config = config |
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self.num_labels = self.config.num_labels |
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self.audio_config: Union[PretrainedConfig, None] = None |
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self.text_config: Union[PretrainedConfig, None] = None |
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self.audio_model: Union[PreTrainedModel, None] = None |
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self.text_model: Union[PreTrainedModel, None] = None |
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self.classifier: Union[torch.nn.Linear, None] = None |
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class FusionModuleQ(torch.nn.Module): |
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def __init__(self, audio_dim, text_dim, num_heads, dropout=0.1): |
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super().__init__() |
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self.dimension = min(audio_dim, text_dim) |
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self.a_self_attention = torch.nn.MultiheadAttention(self.dimension, num_heads=num_heads) |
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self.t_self_attention = torch.nn.MultiheadAttention(self.dimension, num_heads=num_heads) |
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self.audio_norm = torch.nn.LayerNorm(self.dimension) |
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self.text_norm = torch.nn.LayerNorm(self.dimension) |
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def forward(self, audio_output, text_output): |
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audio_attn, _ = self.a_self_attention(audio_output, text_output, text_output) |
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text_attn, _ = self.t_self_attention(text_output, audio_output, audio_output) |
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audio_add = self.audio_norm(audio_output + audio_attn) |
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text_add = self.text_norm(text_output + text_attn) |
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return audio_add, text_add |
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class AudioTextFusionModelForSequenceClassificaion(AudioTextModelForSequenceBaseClassification): |
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def __init__(self, config): |
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""" |
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Args: |
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config (MultiModalConfig): config |
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Attributes: |
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fusion_module_1 (FusionModuleQ): Fusion Module Q 1 |
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fusion_module_2 (FusionModuleQ): Fusion Module Q 2 |
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audio_projector (Union[torch.nn.Linear, None]): Projection layer for audio embeds |
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text_projector (Union[torch.nn.Linear, None]): Projection layer for text embeds |
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audio_avg_pool (Union[torch.nn.AvgPool1d, None]): Audio average pool (out from fusion block) |
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text_avg_pool (Union[torch.nn.AvgPool1d, None]): Text average pool (out from fusion block) |
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""" |
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super().__init__(config) |
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self.fusion_module_1: Union[FusionModuleQ, None] = None |
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self.fusion_module_2: Union[FusionModuleQ, None] = None |
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self.audio_projector: Union[torch.nn.Linear, None] = None |
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self.text_projector: Union[torch.nn.Linear, None] = None |
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self.audio_avg_pool: Union[torch.nn.AvgPool1d, None] = None |
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self.text_avg_pool: Union[torch.nn.AvgPool1d, None] = None |
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class WavLMBertForSequenceClassification(AudioTextFusionModelForSequenceClassificaion): |
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""" |
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WavLMBertForSequenceClassification is a model for sequence classification task |
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(e.g. sentiment analysis, text classification, etc.) for fine-tuning |
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Args: |
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config (WavLMBertConfig): config |
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Attributes: |
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config (WavLMBertConfig): config |
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audio_config (WavLMConfig): wavlm config |
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text_config (BertConfig): bert config |
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audio_model (WavLMModel): wavlm model |
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text_model (BertModel): bert model |
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fusion_module_1 (FusionModuleQ): Fusion Module Q 1 |
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fusion_module_2 (FusionModuleQ): Fusion Module Q 2 |
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audio_projector (Union[torch.nn.Linear, None]): Projection layer for audio embeds |
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text_projector (Union[torch.nn.Linear, None]): Projection layer for text embeds |
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audio_avg_pool (Union[torch.nn.AvgPool1d, None]): Audio average pool (out from fusion block) |
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text_avg_pool (Union[torch.nn.AvgPool1d, None]): Text average pool (out from fusion block) |
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classifier (torch.nn.Linear): classifier |
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""" |
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def __init__(self, config): |
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super().__init__(config) |
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self.supports_gradient_checkpointing = getattr(config, "gradient_checkpointing", True) |
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self.audio_config = WavLMConfig.from_dict(self.config.WavLMModel) |
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self.text_config = BertConfig.from_dict(self.config.BertModel) |
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self.audio_model = WavLMModel(self.audio_config) |
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self.text_model = BertModel(self.text_config) |
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self.fusion_module_1 = FusionModuleQ(self.audio_config.hidden_size, self.text_config.hidden_size, |
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self.config.num_heads, self.config.f_dropout) |
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self.fusion_module_2 = FusionModuleQ(self.audio_config.hidden_size, self.text_config.hidden_size, |
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self.config.num_heads, self.config.f_dropout) |
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self.audio_projector = torch.nn.Linear(self.audio_config.hidden_size, self.text_config.hidden_size) |
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self.text_projector = torch.nn.Linear(self.text_config.hidden_size, self.text_config.hidden_size) |
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self.audio_avg_pool = torch.nn.AvgPool1d(self.config.kernel_size) |
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self.text_avg_pool = torch.nn.AvgPool1d(self.config.kernel_size) |
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cls_dim = min(self.audio_config.hidden_size, self.text_config.hidden_size) |
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self.classifier = torch.nn.Linear( |
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(cls_dim * 2) // self.config.kernel_size, self.config.num_labels |
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) |
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self.init_weights() |
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@staticmethod |
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def _set_gradient_checkpointing(module, value=False): |
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if isinstance(module, (WavLMEncoder, WavLMEncoderStableLayerNorm, WavLMFeatureEncoder, BertEncoder)): |
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module.gradient_checkpointing = value |
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def forward( |
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self, |
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input_ids=None, |
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input_values=None, |
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text_attention_mask=None, |
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audio_attention_mask=None, |
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token_type_ids=None, |
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position_ids=None, |
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head_mask=None, |
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inputs_embeds=None, |
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labels=None, |
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output_attentions=None, |
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output_hidden_states=None, |
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return_dict=True, |
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): |
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"""Forward method for multimodal model for sequence classification task (e.g. text + audio) |
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Args: |
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input_ids (torch.LongTensor, optional): input ids. Defaults to None. |
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input_values (torch.FloatTensor, optional): input values. Defaults to None. |
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text_attention_mask (torch.LongTensor, optional): text attention mask. Defaults to None. |
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audio_attention_mask (torch.LongTensor, optional): audio attention mask. Defaults to None. |
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token_type_ids (torch.LongTensor, optional): token type ids. Defaults to None. |
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position_ids (torch.LongTensor, optional): position ids. Defaults to None. |
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head_mask (torch.FloatTensor, optional): head mask. Defaults to None. |
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inputs_embeds (torch.FloatTensor, optional): inputs embeds. Defaults to None. |
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labels (torch.LongTensor, optional): labels. Defaults to None. |
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output_attentions (bool, optional): output attentions. Defaults to None. |
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output_hidden_states (bool, optional): output hidden states. Defaults to None. |
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return_dict (bool, optional): return dict. Defaults to True. |
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Returns: |
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torch.FloatTensor: logits |
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""" |
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audio_output = self.audio_model( |
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input_values=input_values, |
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attention_mask=audio_attention_mask, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict |
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) |
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text_output = self.text_model( |
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input_ids=input_ids, |
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attention_mask=text_attention_mask, |
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token_type_ids=token_type_ids, |
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position_ids=position_ids, |
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head_mask=head_mask, |
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inputs_embeds=inputs_embeds, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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audio_avg = self.merged_strategy(audio_output.last_hidden_state, mode=self.config.pooling_mode) |
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audio_proj = self.audio_projector(audio_avg) |
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text_proj = self.text_projector(text_output.pooler_output) |
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audio_mha, text_mha = self.fusion_module_1(audio_proj, text_proj) |
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audio_mha, text_mha = self.fusion_module_2(audio_mha, text_mha) |
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audio_avg = self.audio_avg_pool(audio_mha) |
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text_avg = self.text_avg_pool(text_mha) |
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fusion_output = torch.concat((audio_avg, text_avg), dim=1) |
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logits = self.classifier(fusion_output) |
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loss = None |
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if labels is not None: |
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loss = self.compute_loss(logits, labels) |
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return SequenceClassifierOutput( |
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loss=loss, |
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logits=logits |
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) |
