# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) # 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) # 2024 Alibaba Inc (Xiang Lyu) # # 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 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Modified from ESPnet(https://github.com/espnet/espnet) """Encoder definition.""" from typing import Tuple import torch import torch.utils.checkpoint as ckpt from cosyvoice.transformer.convolution import ConvolutionModule from cosyvoice.transformer.encoder_layer import TransformerEncoderLayer from cosyvoice.transformer.encoder_layer import ConformerEncoderLayer from cosyvoice.transformer.positionwise_feed_forward import PositionwiseFeedForward from cosyvoice.utils.class_utils import ( COSYVOICE_EMB_CLASSES, COSYVOICE_SUBSAMPLE_CLASSES, COSYVOICE_ATTENTION_CLASSES, COSYVOICE_ACTIVATION_CLASSES, ) from cosyvoice.utils.mask import make_pad_mask from cosyvoice.utils.mask import add_optional_chunk_mask class BaseEncoder(torch.nn.Module): def __init__( self, input_size: int, output_size: int = 256, attention_heads: int = 4, linear_units: int = 2048, num_blocks: int = 6, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.1, attention_dropout_rate: float = 0.0, input_layer: str = "conv2d", pos_enc_layer_type: str = "abs_pos", normalize_before: bool = True, static_chunk_size: int = 0, use_dynamic_chunk: bool = False, global_cmvn: torch.nn.Module = None, use_dynamic_left_chunk: bool = False, gradient_checkpointing: bool = False, ): """ Args: input_size (int): input dim output_size (int): dimension of attention attention_heads (int): the number of heads of multi head attention linear_units (int): the hidden units number of position-wise feed forward num_blocks (int): the number of decoder blocks dropout_rate (float): dropout rate attention_dropout_rate (float): dropout rate in attention positional_dropout_rate (float): dropout rate after adding positional encoding input_layer (str): input layer type. optional [linear, conv2d, conv2d6, conv2d8] pos_enc_layer_type (str): Encoder positional encoding layer type. opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos] normalize_before (bool): True: use layer_norm before each sub-block of a layer. False: use layer_norm after each sub-block of a layer. static_chunk_size (int): chunk size for static chunk training and decoding use_dynamic_chunk (bool): whether use dynamic chunk size for training or not, You can only use fixed chunk(chunk_size > 0) or dyanmic chunk size(use_dynamic_chunk = True) global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module use_dynamic_left_chunk (bool): whether use dynamic left chunk in dynamic chunk training key_bias: whether use bias in attention.linear_k, False for whisper models. gradient_checkpointing: rerunning a forward-pass segment for each checkpointed segment during backward. """ super().__init__() self._output_size = output_size self.global_cmvn = global_cmvn self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer]( input_size, output_size, dropout_rate, COSYVOICE_EMB_CLASSES[pos_enc_layer_type](output_size, positional_dropout_rate), ) self.normalize_before = normalize_before self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5) self.static_chunk_size = static_chunk_size self.use_dynamic_chunk = use_dynamic_chunk self.use_dynamic_left_chunk = use_dynamic_left_chunk self.gradient_checkpointing = gradient_checkpointing def output_size(self) -> int: return self._output_size def forward( self, xs: torch.Tensor, xs_lens: torch.Tensor, decoding_chunk_size: int = 0, num_decoding_left_chunks: int = -1, ) -> Tuple[torch.Tensor, torch.Tensor]: """Embed positions in tensor. Args: xs: padded input tensor (B, T, D) xs_lens: input length (B) decoding_chunk_size: decoding chunk size for dynamic chunk 0: default for training, use random dynamic chunk. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. num_decoding_left_chunks: number of left chunks, this is for decoding, the chunk size is decoding_chunk_size. >=0: use num_decoding_left_chunks <0: use all left chunks Returns: encoder output tensor xs, and subsampled masks xs: padded output tensor (B, T' ~= T/subsample_rate, D) masks: torch.Tensor batch padding mask after subsample (B, 1, T' ~= T/subsample_rate) NOTE(xcsong): We pass the `__call__` method of the modules instead of `forward` to the checkpointing API because `__call__` attaches all the hooks of the module. https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 """ T = xs.size(1) masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) if self.global_cmvn is not None: xs = self.global_cmvn(xs) xs, pos_emb, masks = self.embed(xs, masks) mask_pad = masks # (B, 1, T/subsample_rate) chunk_masks = add_optional_chunk_mask(xs, masks, self.use_dynamic_chunk, self.use_dynamic_left_chunk, decoding_chunk_size, self.static_chunk_size, num_decoding_left_chunks) if self.gradient_checkpointing and self.training: xs = self.forward_layers_checkpointed(xs, chunk_masks, pos_emb, mask_pad) else: xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad) if self.normalize_before: xs = self.after_norm(xs) # Here we assume the mask is not changed in encoder layers, so just # return the masks before encoder layers, and the masks will be used # for cross attention with decoder later return xs, masks def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, pos_emb: torch.Tensor, mask_pad: torch.Tensor) -> torch.Tensor: for layer in self.encoders: xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) return xs @torch.jit.unused def forward_layers_checkpointed(self, xs: torch.Tensor, chunk_masks: torch.Tensor, pos_emb: torch.Tensor, mask_pad: torch.Tensor) -> torch.Tensor: for layer in self.encoders: xs, chunk_masks, _, _ = ckpt.checkpoint(layer.__call__, xs, chunk_masks, pos_emb, mask_pad) return xs @torch.jit.export def forward_chunk( self, xs: torch.Tensor, offset: int, required_cache_size: int, att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Forward just one chunk Args: xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim), where `time == (chunk_size - 1) * subsample_rate + \ subsample.right_context + 1` offset (int): current offset in encoder output time stamp required_cache_size (int): cache size required for next chunk compuation >=0: actual cache size <0: means all history cache is required att_cache (torch.Tensor): cache tensor for KEY & VALUE in transformer/conformer attention, with shape (elayers, head, cache_t1, d_k * 2), where `head * d_k == hidden-dim` and `cache_t1 == chunk_size * num_decoding_left_chunks`. cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, (elayers, b=1, hidden-dim, cache_t2), where `cache_t2 == cnn.lorder - 1` Returns: torch.Tensor: output of current input xs, with shape (b=1, chunk_size, hidden-dim). torch.Tensor: new attention cache required for next chunk, with dynamic shape (elayers, head, ?, d_k * 2) depending on required_cache_size. torch.Tensor: new conformer cnn cache required for next chunk, with same shape as the original cnn_cache. """ assert xs.size(0) == 1 # tmp_masks is just for interface compatibility tmp_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) tmp_masks = tmp_masks.unsqueeze(1) if self.global_cmvn is not None: xs = self.global_cmvn(xs) # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim) xs, pos_emb, _ = self.embed(xs, tmp_masks, offset) # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim) elayers, cache_t1 = att_cache.size(0), att_cache.size(2) chunk_size = xs.size(1) attention_key_size = cache_t1 + chunk_size pos_emb = self.embed.position_encoding(offset=offset - cache_t1, size=attention_key_size) if required_cache_size < 0: next_cache_start = 0 elif required_cache_size == 0: next_cache_start = attention_key_size else: next_cache_start = max(attention_key_size - required_cache_size, 0) r_att_cache = [] r_cnn_cache = [] for i, layer in enumerate(self.encoders): # NOTE(xcsong): Before layer.forward # shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2), # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) xs, _, new_att_cache, new_cnn_cache = layer( xs, att_mask, pos_emb, att_cache=att_cache[i:i + 1] if elayers > 0 else att_cache, cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache) # NOTE(xcsong): After layer.forward # shape(new_att_cache) is (1, head, attention_key_size, d_k * 2), # shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2) r_att_cache.append(new_att_cache[:, :, next_cache_start:, :]) r_cnn_cache.append(new_cnn_cache.unsqueeze(0)) if self.normalize_before: xs = self.after_norm(xs) # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2), # ? may be larger than cache_t1, it depends on required_cache_size r_att_cache = torch.cat(r_att_cache, dim=0) # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2) r_cnn_cache = torch.cat(r_cnn_cache, dim=0) return (xs, r_att_cache, r_cnn_cache) @torch.jit.unused def forward_chunk_by_chunk( self, xs: torch.Tensor, decoding_chunk_size: int, num_decoding_left_chunks: int = -1, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Forward input chunk by chunk with chunk_size like a streaming fashion Here we should pay special attention to computation cache in the streaming style forward chunk by chunk. Three things should be taken into account for computation in the current network: 1. transformer/conformer encoder layers output cache 2. convolution in conformer 3. convolution in subsampling However, we don't implement subsampling cache for: 1. We can control subsampling module to output the right result by overlapping input instead of cache left context, even though it wastes some computation, but subsampling only takes a very small fraction of computation in the whole model. 2. Typically, there are several covolution layers with subsampling in subsampling module, it is tricky and complicated to do cache with different convolution layers with different subsampling rate. 3. Currently, nn.Sequential is used to stack all the convolution layers in subsampling, we need to rewrite it to make it work with cache, which is not prefered. Args: xs (torch.Tensor): (1, max_len, dim) chunk_size (int): decoding chunk size """ assert decoding_chunk_size > 0 # The model is trained by static or dynamic chunk assert self.static_chunk_size > 0 or self.use_dynamic_chunk subsampling = self.embed.subsampling_rate context = self.embed.right_context + 1 # Add current frame stride = subsampling * decoding_chunk_size decoding_window = (decoding_chunk_size - 1) * subsampling + context num_frames = xs.size(1) att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) outputs = [] offset = 0 required_cache_size = decoding_chunk_size * num_decoding_left_chunks # Feed forward overlap input step by step for cur in range(0, num_frames - context + 1, stride): end = min(cur + decoding_window, num_frames) chunk_xs = xs[:, cur:end, :] (y, att_cache, cnn_cache) = self.forward_chunk(chunk_xs, offset, required_cache_size, att_cache, cnn_cache) outputs.append(y) offset += y.size(1) ys = torch.cat(outputs, 1) masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool) return ys, masks class TransformerEncoder(BaseEncoder): """Transformer encoder module.""" def __init__( self, input_size: int, output_size: int = 256, attention_heads: int = 4, linear_units: int = 2048, num_blocks: int = 6, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.1, attention_dropout_rate: float = 0.0, input_layer: str = "conv2d", pos_enc_layer_type: str = "abs_pos", normalize_before: bool = True, static_chunk_size: int = 0, use_dynamic_chunk: bool = False, global_cmvn: torch.nn.Module = None, use_dynamic_left_chunk: bool = False, key_bias: bool = True, selfattention_layer_type: str = "selfattn", activation_type: str = "relu", gradient_checkpointing: bool = False, ): """ Construct TransformerEncoder See Encoder for the meaning of each parameter. """ super().__init__(input_size, output_size, attention_heads, linear_units, num_blocks, dropout_rate, positional_dropout_rate, attention_dropout_rate, input_layer, pos_enc_layer_type, normalize_before, static_chunk_size, use_dynamic_chunk, global_cmvn, use_dynamic_left_chunk, gradient_checkpointing) activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() self.encoders = torch.nn.ModuleList([ TransformerEncoderLayer( output_size, COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](attention_heads, output_size, attention_dropout_rate, key_bias), PositionwiseFeedForward(output_size, linear_units, dropout_rate, activation), dropout_rate, normalize_before) for _ in range(num_blocks) ]) class ConformerEncoder(BaseEncoder): """Conformer encoder module.""" def __init__( self, input_size: int, output_size: int = 256, attention_heads: int = 4, linear_units: int = 2048, num_blocks: int = 6, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.1, attention_dropout_rate: float = 0.0, input_layer: str = "conv2d", pos_enc_layer_type: str = "rel_pos", normalize_before: bool = True, static_chunk_size: int = 0, use_dynamic_chunk: bool = False, global_cmvn: torch.nn.Module = None, use_dynamic_left_chunk: bool = False, positionwise_conv_kernel_size: int = 1, macaron_style: bool = True, selfattention_layer_type: str = "rel_selfattn", activation_type: str = "swish", use_cnn_module: bool = True, cnn_module_kernel: int = 15, causal: bool = False, cnn_module_norm: str = "batch_norm", key_bias: bool = True, gradient_checkpointing: bool = False, ): """Construct ConformerEncoder Args: input_size to use_dynamic_chunk, see in BaseEncoder positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer. macaron_style (bool): Whether to use macaron style for positionwise layer. selfattention_layer_type (str): Encoder attention layer type, the parameter has no effect now, it's just for configure compatibility. activation_type (str): Encoder activation function type. use_cnn_module (bool): Whether to use convolution module. cnn_module_kernel (int): Kernel size of convolution module. causal (bool): whether to use causal convolution or not. key_bias: whether use bias in attention.linear_k, False for whisper models. """ super().__init__(input_size, output_size, attention_heads, linear_units, num_blocks, dropout_rate, positional_dropout_rate, attention_dropout_rate, input_layer, pos_enc_layer_type, normalize_before, static_chunk_size, use_dynamic_chunk, global_cmvn, use_dynamic_left_chunk, gradient_checkpointing) activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]() # self-attention module definition encoder_selfattn_layer_args = ( attention_heads, output_size, attention_dropout_rate, key_bias, ) # feed-forward module definition positionwise_layer_args = ( output_size, linear_units, dropout_rate, activation, ) # convolution module definition convolution_layer_args = (output_size, cnn_module_kernel, activation, cnn_module_norm, causal) self.encoders = torch.nn.ModuleList([ ConformerEncoderLayer( output_size, COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type]( *encoder_selfattn_layer_args), PositionwiseFeedForward(*positionwise_layer_args), PositionwiseFeedForward( *positionwise_layer_args) if macaron_style else None, ConvolutionModule( *convolution_layer_args) if use_cnn_module else None, dropout_rate, normalize_before, ) for _ in range(num_blocks) ])