ExHuBERT_model.py

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

import torch
import torch.nn as nn
from transformers import HubertForSequenceClassification
from transformers.activations import ACT2FN
from transformers.deepspeed import is_deepspeed_zero3_enabled
from transformers.file_utils import ModelOutput
from transformers.modeling_outputs import BaseModelOutput
from transformers.models.hubert import HubertConfig
from transformers.models.hubert.modeling_hubert import HubertPreTrainedModel, HubertFeatureEncoder, \
    HubertFeatureProjection, _compute_mask_indices, \
    HubertPositionalConvEmbedding, HubertAttention
import torch.nn.functional as F
from huggingface_hub import PyTorchModelHubMixin

######
#
#######



_HIDDEN_STATES_START_POSITION = 1

# General docstring
_CONFIG_FOR_DOC = "HubertConfig"

# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/hubert-large-ls960-ft"
_EXPECTED_OUTPUT_SHAPE = [1, 292, 768]

# CTC docstring
_CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'"
_CTC_EXPECTED_LOSS = 22.68

# Audio class docstring
_SEQ_CLASS_CHECKPOINT = "superb/hubert-base-superb-ks"
_SEQ_CLASS_EXPECTED_OUTPUT = "'_unknown_'"
_SEQ_CLASS_EXPECTED_LOSS = 8.53

HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/hubert-base-ls960",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
]


# SwiGLU function
# From """GLU Variants Improve Transformer """
# https://doi.org/10.48550/arXiv.2002.05202
class SwiGLU(nn.Module):
    def forward(self, x):
        x, gate = x.chunk(2, dim=-1)
        return F.silu(gate) * x


@dataclass
class SpeechClassifierOutput(ModelOutput):
    """
    Speech Classifier Output dataclass
    """
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


class ExHuBERTFeedForward(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.intermediate_dropout = nn.Dropout(config.activation_dropout)

        self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

        self.output_dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.output_dropout = nn.Dropout(config.hidden_dropout)

    def forward(self, hidden_states):
        hidden_states = self.intermediate_dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        hidden_states = self.intermediate_dropout(hidden_states)

        hidden_states = self.output_dense(hidden_states)
        hidden_states = self.output_dropout(hidden_states)
        return hidden_states


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2EncoderLayer with Wav2Vec2->Hubert
class ExHuBERTEncoderLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.attention = HubertAttention(
            embed_dim=config.hidden_size,
            num_heads=config.num_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=False,
        )
        self.dropout = nn.Dropout(config.hidden_dropout)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.feed_forward = ExHuBERTFeedForward(config)
        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.gate_bb_linear = nn.Linear(config.hidden_size, config.hidden_size)

    def forward(
            self,
            hidden_states: torch.Tensor,
            attention_mask: Optional[torch.Tensor] = None,
            output_attentions: bool = False,
    ):
        attn_residual = hidden_states
        hidden_states = self.layer_norm(hidden_states)
        hidden_states, attn_weights, _ = self.attention(
            hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
        )
        hidden_states = self.dropout(hidden_states)
        hidden_states = attn_residual + hidden_states
        hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states))

        hidden_states = self.gate_bb_linear(hidden_states)
        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class ExHuBERTEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.pos_conv_embed = HubertPositionalConvEmbedding(config)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout)
        self.layers = nn.ModuleList(
            [ExHuBERTEncoderLayer(config) for _ in range(config.num_hidden_layers)]
        )
        self.gradient_checkpointing = False

    def forward(
            self,
            hidden_states,
            attention_mask=None,
            output_attentions=False,
            output_hidden_states=False,
            return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        if attention_mask is not None:
            # make sure padded tokens are not attended to
            expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
            hidden_states[~expand_attention_mask] = 0

            # extend attention_mask
            attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)
            attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min
            attention_mask = attention_mask.expand(
                attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
            )

        position_embeddings = self.pos_conv_embed(hidden_states)
        hidden_states = hidden_states + position_embeddings
        hidden_states = self.dropout(hidden_states)

        deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()

        skip = torch.zeros_like(hidden_states)
        skip_bool = False
        for layer in self.layers:

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = torch.rand([])

            # skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
            skip_the_layer = False
            if not skip_the_layer or deepspeed_zero3_is_enabled:
                # under deepspeed zero3 all gpus must run in sync
                # XXX: could optimize this like synced_gpus in generate_utils but not sure if it's worth the code complication
                if self.gradient_checkpointing and self.training:
                    # create gradient checkpointing function
                    def create_custom_forward(module):
                        def custom_forward(*inputs):
                            return module(*inputs, output_attentions)

                        return custom_forward

                    layer_outputs = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(layer),
                        hidden_states,
                        attention_mask,
                    )
                else:
                    layer_outputs = layer(
                        hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
                    )
                hidden_states = layer_outputs[0]

            if skip_the_layer:
                layer_outputs = (None, None)

            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)
            if skip_bool is True:
                hidden_states = hidden_states + skip

                skip_bool = False
            else:
                skip = hidden_states
                skip_bool = True

        hidden_states = self.layer_norm(hidden_states)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class ExHuBERT_model_(HubertPreTrainedModel):
    def __init__(self, config: HubertConfig):
        super().__init__(config)
        setattr(config, 'num_hidden_layers', 48)
        self.config = config
        self.feature_extractor = HubertFeatureEncoder(config)
        self.feature_projection = HubertFeatureProjection(config)

        if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
            self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())

        self.encoder = ExHuBERTEncoder(config)

        # Initialize weights and apply final processing
        self.post_init()

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states
    def _mask_hidden_states(
            self,
            hidden_states: torch.FloatTensor,
            mask_time_indices: Optional[torch.FloatTensor] = None,
            attention_mask: Optional[torch.LongTensor] = None,
    ):
        """
        Masks extracted features along time axis and/or along feature axis according to
        [SpecAugment](https://arxiv.org/abs/1904.08779).
        """

        # `config.apply_spec_augment` can set masking to False
        if not getattr(self.config, "apply_spec_augment", True):
            return hidden_states

        # generate indices & apply SpecAugment along time axis
        batch_size, sequence_length, hidden_size = hidden_states.size()

        if mask_time_indices is not None:
            # apply SpecAugment along time axis with given mask_time_indices
            hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
        elif self.config.mask_time_prob > 0 and self.training:
            mask_time_indices = _compute_mask_indices(
                (batch_size, sequence_length),
                mask_prob=self.config.mask_time_prob,
                mask_length=self.config.mask_time_length,
                attention_mask=attention_mask,
                min_masks=self.config.mask_time_min_masks,
            )
            mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool)
            hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)

        if self.config.mask_feature_prob > 0 and self.training:
            # generate indices & apply SpecAugment along feature axis
            mask_feature_indices = _compute_mask_indices(
                (batch_size, hidden_size),
                mask_prob=self.config.mask_feature_prob,
                mask_length=self.config.mask_feature_length,
                min_masks=self.config.mask_feature_min_masks,
            )
            mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool)
            mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1)
            hidden_states[mask_feature_indices] = 0

        return hidden_states

    def forward(
            self,
            input_values: Optional[torch.Tensor],
            attention_mask: Optional[torch.Tensor] = None,
            mask_time_indices: Optional[torch.FloatTensor] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:

        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

        extract_features = self.feature_extractor(input_values)
        extract_features = extract_features.transpose(1, 2)

        if attention_mask is not None:
            # compute reduced attention_mask corresponding to feature vectors
            attention_mask = self._get_feature_vector_attention_mask(extract_features.shape[1], attention_mask)

        hidden_states = self.feature_projection(extract_features)
        hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices)

        encoder_outputs = self.encoder(
            hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = encoder_outputs[0]

        if not return_dict:
            return (hidden_states,) + encoder_outputs[1:]

        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class ExHuBERT(HubertPreTrainedModel,PyTorchModelHubMixin):
    def __init__(self, config):
        super().__init__(config)
        setattr(config, "num_labels", 6)
        if hasattr(config, "add_adapter") and config.add_adapter:
            raise ValueError(
                "Sequence classification does not support the use of Hubert adapters (config.add_adapter=True)"
            )
        self.hubert = ExHuBERT_model_(config)
        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
        if config.use_weighted_layer_sum:
            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
        self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
        self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    def freeze_feature_encoder(self):
        """
        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
        not be updated during training.
        """
        self.hubert.feature_extractor._freeze_parameters()

    def freeze_base_model(self):
        """
        Calling this function will disable the gradient computation for the base model so that its parameters will not
        be updated during training. Only the classification head will be updated.
        """
        for param in self.hubert.parameters():
            param.requires_grad = False

    def forward(
            self,
            input_values: Optional[torch.Tensor],
            attention_mask: Optional[torch.Tensor] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            labels: Optional[torch.Tensor] = None,
    ) -> Union[Tuple, SpeechClassifierOutput]:
        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
        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states

        outputs = self.hubert(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if self.config.use_weighted_layer_sum:
            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
            hidden_states = torch.stack(hidden_states, dim=1)
            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
        else:
            hidden_states = outputs[0]

        hidden_states = self.projector(hidden_states)
        if attention_mask is None:
            pooled_output = hidden_states.mean(dim=1)
        else:
            padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
            hidden_states[~padding_mask] = 0.0
            pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)

        logits = self.classifier(pooled_output)

        loss = None

        if not return_dict:
            output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
            return ((loss,) + output) if loss is not None else output

        return SpeechClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def freeze_og_encoder(self):
        for param in self.hubert.encoder.layers[::2].parameters():
            param.requires_grad = False

    def print_trainable_parameters(model):
        '''
        prints all trainable parameters of a model
        '''
        trainable_params = 0
        all_param = 0
        for _, param in model.named_parameters():
            all_param += param.numel()
            if param.requires_grad:
                trainable_params += param.numel()
        print(
            f"trainable params: {trainable_params:,d} || all params: {all_param:,d} || trainable%: {100 * trainable_params / all_param:.2f}"
        )