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# Copyright 2024 The YourMT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Please see the details in the LICENSE file.
from typing import Tuple, Literal, Any, Optional
import math
import torch
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutput
from model.conformer_helper import ConformerYMT3Config, ConformerYMT3PreTrainedModel
from model.positional_encoding import (Wav2Vec2ConformerRelPositionalEmbedding,
Wav2Vec2ConformerRotaryPositionalEmbedding)
class ConformerYMT3FeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.intermediate_dropout = nn.Dropout(config.dropout_rate)
self.intermediate_dense = nn.Linear(config.d_model, 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.d_model)
self.output_dropout = nn.Dropout(config.dropout_rate)
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
class ConformerYMT3ConvolutionModule(nn.Module):
"""Convolution block used in the conformer block"""
def __init__(self, config):
super().__init__()
if (config.conv_depthwise_kernel_size - 1) % 2 == 1:
raise ValueError("`config.conv_depthwise_kernel_size` should be a odd number for 'SAME' padding")
self.layer_norm = nn.LayerNorm(config.d_model)
self.pointwise_conv1 = torch.nn.Conv1d(
config.d_model,
2 * config.d_model,
kernel_size=1,
stride=1,
padding=0,
bias=False,
)
self.glu = torch.nn.GLU(dim=1)
self.depthwise_conv = torch.nn.Conv1d(
config.d_model,
config.d_model,
config.conv_depthwise_kernel_size,
stride=1,
padding=(config.conv_depthwise_kernel_size - 1) // 2,
groups=config.d_model,
bias=False,
)
self.batch_norm = torch.nn.BatchNorm1d(config.d_model)
self.activation = ACT2FN[config.hidden_act]
self.pointwise_conv2 = torch.nn.Conv1d(
config.d_model,
config.d_model,
kernel_size=1,
stride=1,
padding=0,
bias=False,
)
self.dropout = torch.nn.Dropout(config.dropout_rate)
def forward(self, hidden_states):
hidden_states = self.layer_norm(hidden_states)
# exchange the temporal dimension and the feature dimension
hidden_states = hidden_states.transpose(1, 2)
# GLU mechanism
# => (batch, 2*channel, dim)
hidden_states = self.pointwise_conv1(hidden_states)
# => (batch, channel, dim)
hidden_states = self.glu(hidden_states)
# 1D Depthwise Conv
hidden_states = self.depthwise_conv(hidden_states)
hidden_states = self.batch_norm(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.pointwise_conv2(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
return hidden_states
class ConformerYMT3SelfAttention(nn.Module):
"""Construct a ConformerSelfAttention object.
Can be enhanced with rotary or relative position embeddings.
"""
def __init__(self, config):
super().__init__()
self.head_size = config.d_model // config.num_heads
self.num_heads = config.num_heads
self.position_encoding_type = config.position_encoding_type
self.linear_q = nn.Linear(config.d_model, config.d_model)
self.linear_k = nn.Linear(config.d_model, config.d_model)
self.linear_v = nn.Linear(config.d_model, config.d_model)
self.linear_out = nn.Linear(config.d_model, config.d_model)
self.dropout = nn.Dropout(p=config.dropout_rate)
if self.position_encoding_type == "relative":
# linear transformation for positional encoding
self.linear_pos = nn.Linear(config.d_model, config.d_model, bias=False)
# these two learnable bias are used in matrix c and matrix d
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
self.pos_bias_u = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
self.pos_bias_v = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
relative_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# self-attention mechanism
batch_size, sequence_length, d_model = hidden_states.size()
# make sure query/key states can be != value states
query_key_states = hidden_states
value_states = hidden_states
if self.position_encoding_type == "rotary":
if relative_position_embeddings is None:
raise ValueError(
"`relative_position_embeddings` has to be defined when `self.position_encoding_type == 'rotary'")
query_key_states = self._apply_rotary_embedding(query_key_states, relative_position_embeddings)
# project query_key_states and value_states
query = self.linear_q(query_key_states).view(batch_size, -1, self.num_heads, self.head_size)
key = self.linear_k(query_key_states).view(batch_size, -1, self.num_heads, self.head_size)
value = self.linear_v(value_states).view(batch_size, -1, self.num_heads, self.head_size)
# => (batch, head, time1, d_k)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
if self.position_encoding_type == "relative":
if relative_position_embeddings is None:
raise ValueError("`relative_position_embeddings` has to be defined when `self.position_encoding_type =="
" 'relative'")
# apply relative_position_embeddings to qk scores
# as proposed in Transformer_XL: https://arxiv.org/abs/1901.02860
scores = self._apply_relative_embeddings(query=query,
key=key,
relative_position_embeddings=relative_position_embeddings)
else:
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_size)
# apply attention_mask if necessary
if attention_mask is not None:
scores = scores + attention_mask
# => (batch, head, time1, time2)
probs = torch.softmax(scores, dim=-1)
probs = self.dropout(probs)
# => (batch, head, time1, d_k)
hidden_states = torch.matmul(probs, value)
# => (batch, time1, d_model)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_size)
hidden_states = self.linear_out(hidden_states)
return hidden_states, probs
def _apply_rotary_embedding(self, hidden_states, relative_position_embeddings):
batch_size, sequence_length, d_model = hidden_states.size()
hidden_states = hidden_states.view(batch_size, sequence_length, self.num_heads, self.head_size)
cos = relative_position_embeddings[0, :sequence_length, ...]
sin = relative_position_embeddings[1, :sequence_length, ...]
# rotate hidden_states with rotary embeddings
hidden_states = hidden_states.transpose(0, 1)
rotated_states_begin = hidden_states[..., :self.head_size // 2]
rotated_states_end = hidden_states[..., self.head_size // 2:]
rotated_states = torch.cat((-rotated_states_end, rotated_states_begin), dim=rotated_states_begin.ndim - 1)
hidden_states = (hidden_states * cos) + (rotated_states * sin)
hidden_states = hidden_states.transpose(0, 1)
hidden_states = hidden_states.view(batch_size, sequence_length, self.num_heads * self.head_size)
return hidden_states
def _apply_relative_embeddings(self, query, key, relative_position_embeddings):
# 1. project positional embeddings
# => (batch, head, 2*time1-1, d_k)
proj_relative_position_embeddings = self.linear_pos(relative_position_embeddings)
proj_relative_position_embeddings = proj_relative_position_embeddings.view(relative_position_embeddings.size(0),
-1, self.num_heads, self.head_size)
proj_relative_position_embeddings = proj_relative_position_embeddings.transpose(1, 2)
proj_relative_position_embeddings = proj_relative_position_embeddings.transpose(2, 3)
# 2. Add bias to query
# => (batch, head, time1, d_k)
query = query.transpose(1, 2)
q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2)
q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2)
# 3. attention score: first compute matrix a and matrix c
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
# => (batch, head, time1, time2)
scores_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1))
# 4. then compute matrix b and matrix d
# => (batch, head, time1, 2*time1-1)
scores_bd = torch.matmul(q_with_bias_v, proj_relative_position_embeddings)
# 5. shift matrix b and matrix d
zero_pad = torch.zeros((*scores_bd.size()[:3], 1), device=scores_bd.device, dtype=scores_bd.dtype)
scores_bd_padded = torch.cat([zero_pad, scores_bd], dim=-1)
scores_bd_padded_shape = scores_bd.size()[:2] + (scores_bd.shape[3] + 1, scores_bd.shape[2])
scores_bd_padded = scores_bd_padded.view(*scores_bd_padded_shape)
scores_bd = scores_bd_padded[:, :, 1:].view_as(scores_bd)
scores_bd = scores_bd[:, :, :, :scores_bd.size(-1) // 2 + 1]
# 6. sum matrices
# => (batch, head, time1, time2)
scores = (scores_ac + scores_bd) / math.sqrt(self.head_size)
return scores
class ConformerYMT3EncoderLayer(nn.Module):
"""Conformer block based on https://arxiv.org/abs/2005.08100."""
def __init__(self, config):
super().__init__()
embed_dim = config.d_model
dropout = config.dropout_rate
# Feed-forward 1
self.ffn1_layer_norm = nn.LayerNorm(embed_dim)
self.ffn1 = ConformerYMT3FeedForward(config)
# Self-Attention
self.self_attn_layer_norm = nn.LayerNorm(embed_dim)
self.self_attn_dropout = torch.nn.Dropout(dropout)
self.self_attn = ConformerYMT3SelfAttention(config)
# Conformer Convolution
self.conv_module = ConformerYMT3ConvolutionModule(config)
# Feed-forward 2
self.ffn2_layer_norm = nn.LayerNorm(embed_dim)
self.ffn2 = ConformerYMT3FeedForward(config)
self.final_layer_norm = nn.LayerNorm(embed_dim)
def forward(
self,
hidden_states,
attention_mask: Optional[torch.Tensor] = None,
relative_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
):
hidden_states = hidden_states
# 1. Feed-Forward 1 layer
residual = hidden_states
hidden_states = self.ffn1_layer_norm(hidden_states)
hidden_states = self.ffn1(hidden_states)
hidden_states = hidden_states * 0.5 + residual
residual = hidden_states
# 2. Self-Attention layer
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weigts = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
relative_position_embeddings=relative_position_embeddings,
output_attentions=output_attentions,
)
hidden_states = self.self_attn_dropout(hidden_states)
hidden_states = hidden_states + residual
# 3. Convolutional Layer
residual = hidden_states
hidden_states = self.conv_module(hidden_states)
hidden_states = residual + hidden_states
# 4. Feed-Forward 2 Layer
residual = hidden_states
hidden_states = self.ffn2_layer_norm(hidden_states)
hidden_states = self.ffn2(hidden_states)
hidden_states = hidden_states * 0.5 + residual
hidden_states = self.final_layer_norm(hidden_states)
return hidden_states, attn_weigts
class ConformerYMT3Encoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
if config.position_encoding_type == "relative":
self.embed_positions = Wav2Vec2ConformerRelPositionalEmbedding(config)
elif config.position_encoding_type == "rotary":
self.embed_positions = Wav2Vec2ConformerRotaryPositionalEmbedding(config)
else:
self.embed_positions = None
# self.pos_conv_embed = Wav2Vec2ConformerPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.dropout_rate)
self.layers = nn.ModuleList([ConformerYMT3EncoderLayer(config) for _ in range(config.num_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds: torch.FloatTensor, # (B, T, D)
attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
):
if output_attentions is None:
output_attentions = self.config.output_attentions
if output_hidden_states is None:
output_hidden_states = self.config.output_hidden_states
if return_dict is None:
return_dict = self.config.use_return_dict
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
# inputs_embeds as hidden_states
hidden_states = inputs_embeds
if attention_mask is not None:
# make sure padded tokens output 0
hidden_states[~attention_mask] = 0.0
# 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])
hidden_states = self.dropout(hidden_states)
if self.embed_positions is not None:
relative_position_embeddings = self.embed_positions(hidden_states)
else:
relative_position_embeddings = None
for i, layer in enumerate(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
if not skip_the_layer:
# under deepspeed zero3 all gpus must run in sync
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,
relative_position_embeddings,
)
else:
layer_outputs = layer(
hidden_states,
attention_mask=attention_mask,
relative_position_embeddings=relative_position_embeddings,
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],)
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,
)
def test():
import torch
from model.conformer_mod import ConformerYMT3Encoder
from model.conformer_helper import ConformerYMT3Config
from model.ops import count_parameters
config = ConformerYMT3Config()
encoder = ConformerYMT3Encoder(config)
encoder.eval()
# num params: 48,468,992 w/ intermediate_size=2048
# num params: 23,278,592 w/ intermediate_size=512
x = torch.randn(2, 256, 512) # (B, T, D)
enc_hs = encoder.forward(inputs_embeds=x)['last_hidden_state'] # (B, T, D)
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