Add modeling_MERT.py

modeling_MERT.py

@@ -0,0 +1,408 @@
+"""
+MERT model definition.
+
+Adapted from: https://github.com/yizhilll/MERT/blob/main/scripts/mert_hf/modeling_MERT.py
+"""
+
+from typing import Optional, Tuple, Union
+from transformers.modeling_outputs import BaseModelOutput
+import torch
+from torch import nn
+
+from transformers.models.hubert.modeling_hubert import (
+    HubertFeatureEncoder,
+    HubertModel,
+    HubertEncoderStableLayerNorm,
+    HubertEncoder,
+    HubertEncoderLayer,
+    HubertPositionalConvEmbedding,
+    HubertAttention,
+    HubertFeedForward,
+)
+
+try:
+    from nnAudio import features as nnAudioFeatures
+    NNAUDIO_INSTALLED=True
+except:
+    print("WARNING: feature_extractor_cqt requires the libray 'nnAudio'")
+    NNAUDIO_INSTALLED=False
+
+from .configuration_MERT import MERTConfig
+
+class MERTFeatureProjection(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.feat_proj_layer_norm = config.feat_proj_layer_norm
+        self.feature_extractor_cqt = config.feature_extractor_cqt
+
+        if self.feature_extractor_cqt:
+            # v3 concat features
+            self.feature_dimension = config.conv_dim[-1] + config.feature_extractor_cqt_bins
+            print(f"feature dimention: {self.feature_dimension}")
+        else:
+            self.feature_dimension = config.conv_dim[-1]
+        if self.feat_proj_layer_norm:
+            self.layer_norm = nn.LayerNorm(self.feature_dimension, eps=config.layer_norm_eps)
+        self.projection = nn.Linear(self.feature_dimension, config.hidden_size)
+        self.dropout = nn.Dropout(config.feat_proj_dropout)
+
+    def forward(self, hidden_states):
+        # non-projected hidden states are needed for quantization
+        if self.feat_proj_layer_norm:
+            hidden_states = self.layer_norm(hidden_states)
+        hidden_states = self.projection(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+class MERTModel(HubertModel):
+    # overwrite config class
+    config_class = MERTConfig
+    base_model_prefix = "mert_model"
+    def __init__(
+        self,
+        config: MERTConfig,
+    ) -> None:
+        """ 
+        initialize the with the grandparent method HubertPreTrainedModel.__init__()
+        and modify the HuBERTModel.__init__()
+        """
+        super(HubertModel, self).__init__(config)
+
+        self.config = config
+
+        self.feature_extractor = HubertFeatureEncoder(config)
+        self.feature_projection = MERTFeatureProjection(config) # replace Feature Projection for introcuing new feature
+
+        if self.config.feature_extractor_cqt:
+            assert NNAUDIO_INSTALLED, "ERROR: feature_extractor_cqt requires the libray 'nnAudio', try after `pip install nnAudio` "
+            print('initializing cqt extractor for MERT')            
+            self.feature_extractor_cqt = nnAudioFeatures.cqt.CQT(sr=self.config.sample_rate, hop_length=self.config.sample_rate//50, fmin=32.7, 
+                    fmax=None, n_bins=self.config.feature_extractor_cqt_bins, bins_per_octave=self.config.feature_extractor_cqt_bins//7, 
+                    filter_scale=1, norm=1, window='hann', center=True, 
+                    pad_mode='constant', trainable=False, 
+                    output_format='Magnitude', verbose=True)
+
+        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_())
+
+        
+        if config.do_stable_layer_norm:
+            assert not config.deepnorm, "must use post-layer_norm with deepnorm"
+            self.encoder = HubertEncoderStableLayerNorm(config)
+        else:
+            if config.deepnorm:
+                self.encoder = HubertEncoder_extend(config)
+            else:
+                self.encoder = HubertEncoder(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+    
+    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]:
+        
+        # return super().forward(input_values, attention_mask, mask_time_indices, output_attentions, output_hidden_states, return_dict)
+        
+        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)
+
+        # add additional cqt features for transformer input
+        if self.config.feature_extractor_cqt:
+            features_cqt = self.feature_extractor_cqt(input_values).transpose(1, 2)
+            features_cqt = features_cqt[:,:extract_features.shape[1],:] # align shape
+            # # v2
+            # features_cqt = self.post_cqt_feature_proj(features_cqt)
+            # extract_features = self.feature_projection.layer_norm(extract_features) + self.feature_projection.layer_norm(features_cqt) #v2
+            # v3
+            extract_features = torch.cat([extract_features,features_cqt], 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] # take last_hidden from encoder output
+
+        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 HubertEncoder_extend(HubertEncoder):
+    def __init__(self, config):
+        # super().__init__()
+        # call nn module initialization
+        nn.Module.__init__(self)
+        # super(HubertEncoder_extend, self).__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([HubertEncoderLayerExtend(config) for _ in range(config.num_hidden_layers)])
+
+        self.gradient_checkpointing = False
+
+        if config.deepnorm:
+            import math
+            init_scale = math.pow(8.0 * config.num_hidden_layers, 0.25)
+            for name, p in self.named_parameters():
+                if (
+                    "feed_forward.intermediate_dense" in name
+                    or "feed_forward.output_dense" in name
+                    or "out_proj" in name
+                    or "v_proj" in name
+                ):
+                    p.data.div_(init_scale)
+
+class HubertEncoderLayerExtend(HubertEncoderLayer):
+    def __init__(self, config):
+        nn.Module.__init__(self)
+        # super(HubertEncoderLayerExtend, self).__init__()
+        if config.attention_relax > 0 :
+            self.attention = HubertAttention_extend(
+                embed_dim=config.hidden_size,
+                num_heads=config.num_attention_heads,
+                dropout=config.attention_dropout,
+                is_decoder=False,
+                attention_relax=config.attention_relax,
+            )
+        else:    
+            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 = HubertFeedForward(config)
+        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        if config.deepnorm:
+            import math
+            self.residual_alpha = math.pow(2.0 * config.num_hidden_layers, 0.25)
+        else:
+            self.residual_alpha = 1.0
+    
+    def residual_connection(self, x, residual):
+        '''
+        residual: input before f()
+        x: output of f(residual)
+        '''
+        return residual * self.residual_alpha + x
+
+    def forward(self, hidden_states, attention_mask=None, output_attentions=False):
+        attn_residual = 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 = self.residual_connection(hidden_states, attn_residual)
+
+        hidden_states = self.layer_norm(hidden_states)
+
+        # hidden_states = hidden_states + self.feed_forward(hidden_states)
+        ffn_residual = hidden_states
+        hidden_states = self.feed_forward(hidden_states)
+        hidden_states = self.residual_connection(hidden_states, ffn_residual)
+
+        hidden_states = self.final_layer_norm(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs
+
+
+class HubertAttention_extend(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        dropout: float = 0.0,
+        is_decoder: bool = False,
+        bias: bool = True,
+        attention_relax: float = -1.0,
+    ):
+        super().__init__()
+        # nn.Module.__init__(self)
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+
+        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.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        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)
+
+        if attention_relax > 0:
+            self.attention_relax = attention_relax
+
+    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[Tuple[torch.Tensor]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        layer_head_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ) -> 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.q_proj(hidden_states) * self.scaling
+        # get key, value proj
+        # `past_key_value[0].shape[2] == key_value_states.shape[1]`
+        # is checking that the `sequence_length` of the `past_key_value` is the same as
+        # the provided `key_value_states` to support prefix tuning
+        if (
+            is_cross_attention
+            and past_key_value is not None
+            and past_key_value[0].shape[2] == key_value_states.shape[1]
+        ):
+            # reuse k,v, cross_attentions
+            key_states = past_key_value[0]
+            value_states = past_key_value[1]
+        elif is_cross_attention:
+            # cross_attentions
+            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
+            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
+        elif past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+        else:
+            # self_attention
+            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+
+        if self.is_decoder:
+            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+            # Further calls to cross_attention layer can then reuse all cross-attention
+            # key/value_states (first "if" case)
+            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+            # all previous decoder key/value_states. Further calls to uni-directional self-attention
+            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+            # if encoder bi-directional self-attention `past_key_value` is always `None`
+            past_key_value = (key_states, value_states)
+
+        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
+        key_states = key_states.view(*proj_shape)
+        value_states = value_states.view(*proj_shape)
+
+        src_len = key_states.size(1)
+        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
+
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        if self.attention_relax > 0:
+            # => (bsz, self.num_heads, tgt_len, src_len)
+            # attn_weights_relax = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)/self.attention_relax
+            # => (bsz*self.num_heads, tgt_len, src_len)
+            attn_weights_relax = attn_weights / self.attention_relax
+
+            # => (bsz* self.num_heads, tgt_len, 1)
+            attn_max_relax = torch.max(attn_weights_relax, dim=-1, keepdim=False).unsqueeze(2)
+            attn_weights = (attn_weights_relax - attn_max_relax) * self.attention_relax
+
+        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.view(bsz, self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        if output_attentions:
+            # this operation is a bit awkward, but it's required to
+            # make sure that attn_weights keeps its gradient.
+            # In order to do so, attn_weights have to be reshaped
+            # twice and have to be reused in the following
+            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
+        else:
+            attn_weights_reshaped = None
+
+        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+        attn_output = torch.bmm(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.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        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 aross 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_reshaped, past_key_value