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"""
MERT model definition.
We largely adapt codes from:
1. https://github.com/huggingface/transformers/blob/main/src/transformers/models/hubert/modeling_hubert.py
2. https://github.com/facebookresearch/fairseq/blob/main/fairseq/models/wav2vec/wav2vec2.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