# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) # 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) # 2022 58.com(Wuba) Inc AI Lab. # # 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 EfficientConformer(https://github.com/burchim/EfficientConformer) # Paper(https://arxiv.org/abs/2109.01163) """Encoder definition.""" from typing import Tuple, Optional, List, Union import torch import logging import torch.nn.functional as F from wenet.transformer.positionwise_feed_forward import PositionwiseFeedForward from wenet.transformer.encoder_layer import ConformerEncoderLayer from wenet.efficient_conformer.convolution import ConvolutionModule from wenet.efficient_conformer.encoder_layer import StrideConformerEncoderLayer from wenet.utils.mask import make_pad_mask from wenet.utils.mask import add_optional_chunk_mask from wenet.utils.class_utils import ( WENET_ATTENTION_CLASSES, WENET_EMB_CLASSES, WENET_SUBSAMPLE_CLASSES, WENET_ACTIVATION_CLASSES, ) class EfficientConformerEncoder(torch.nn.Module): """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, macaron_style: bool = True, activation_type: str = "swish", use_cnn_module: bool = True, cnn_module_kernel: int = 15, causal: bool = False, cnn_module_norm: str = "batch_norm", stride_layer_idx: Optional[Union[int, List[int]]] = 3, stride: Optional[Union[int, List[int]]] = 2, group_layer_idx: Optional[Union[int, List[int], tuple]] = (0, 1, 2, 3), group_size: int = 3, stride_kernel: bool = True, **kwargs): """Construct Efficient Conformer Encoder Args: input_size to use_dynamic_chunk, see in BaseEncoder macaron_style (bool): Whether to use macaron style for positionwise layer. 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. stride_layer_idx (list): layer id with StrideConv, start from 0 stride (list): stride size of each StrideConv in efficient conformer group_layer_idx (list): layer id with GroupedAttention, start from 0 group_size (int): group size of every GroupedAttention layer stride_kernel (bool): default True. True: recompute cnn kernels with stride. """ super().__init__() self._output_size = output_size logging.info( f"input_layer = {input_layer}, " f"subsampling_class = {WENET_SUBSAMPLE_CLASSES[input_layer]}") self.global_cmvn = global_cmvn self.embed = WENET_SUBSAMPLE_CLASSES[input_layer]( input_size, output_size, dropout_rate, WENET_EMB_CLASSES[pos_enc_layer_type](output_size, positional_dropout_rate), ) self.input_layer = input_layer 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 activation = WENET_ACTIVATION_CLASSES[activation_type]() self.num_blocks = num_blocks self.attention_heads = attention_heads self.cnn_module_kernel = cnn_module_kernel self.global_chunk_size = 0 self.chunk_feature_map = 0 # efficient conformer configs self.stride_layer_idx = [stride_layer_idx] \ if type(stride_layer_idx) == int else stride_layer_idx self.stride = [stride] \ if type(stride) == int else stride self.group_layer_idx = [group_layer_idx] \ if type(group_layer_idx) == int else group_layer_idx self.grouped_size = group_size # group size of every GroupedAttention layer assert len(self.stride) == len(self.stride_layer_idx) self.cnn_module_kernels = [cnn_module_kernel ] # kernel size of each StridedConv for i in self.stride: if stride_kernel: self.cnn_module_kernels.append(self.cnn_module_kernels[-1] // i) else: self.cnn_module_kernels.append(self.cnn_module_kernels[-1]) logging.info(f"stride_layer_idx= {self.stride_layer_idx}, " f"stride = {self.stride}, " f"cnn_module_kernel = {self.cnn_module_kernels}, " f"group_layer_idx = {self.group_layer_idx}, " f"grouped_size = {self.grouped_size}") # feed-forward module definition positionwise_layer = PositionwiseFeedForward positionwise_layer_args = ( output_size, linear_units, dropout_rate, activation, ) # convolution module definition convolution_layer = ConvolutionModule # encoder definition index = 0 layers = [] for i in range(num_blocks): # self-attention module definition if i in self.group_layer_idx: encoder_selfattn_layer = WENET_ATTENTION_CLASSES[ "grouped_rel_selfattn"] encoder_selfattn_layer_args = (attention_heads, output_size, attention_dropout_rate, self.grouped_size) else: if pos_enc_layer_type == "no_pos": encoder_selfattn_layer = WENET_ATTENTION_CLASSES[ "selfattn"] else: encoder_selfattn_layer = WENET_ATTENTION_CLASSES[ "rel_selfattn"] encoder_selfattn_layer_args = (attention_heads, output_size, attention_dropout_rate) # conformer module definition if i in self.stride_layer_idx: # conformer block with downsampling convolution_layer_args_stride = ( output_size, self.cnn_module_kernels[index], activation, cnn_module_norm, causal, True, self.stride[index]) layers.append( StrideConformerEncoderLayer( output_size, encoder_selfattn_layer(*encoder_selfattn_layer_args), positionwise_layer(*positionwise_layer_args), positionwise_layer(*positionwise_layer_args) if macaron_style else None, convolution_layer(*convolution_layer_args_stride) if use_cnn_module else None, torch.nn.AvgPool1d( kernel_size=self.stride[index], stride=self.stride[index], padding=0, ceil_mode=True, count_include_pad=False), # pointwise_conv_layer dropout_rate, normalize_before, )) index = index + 1 else: # conformer block convolution_layer_args_normal = ( output_size, self.cnn_module_kernels[index], activation, cnn_module_norm, causal) layers.append( ConformerEncoderLayer( output_size, encoder_selfattn_layer(*encoder_selfattn_layer_args), positionwise_layer(*positionwise_layer_args), positionwise_layer(*positionwise_layer_args) if macaron_style else None, convolution_layer(*convolution_layer_args_normal) if use_cnn_module else None, dropout_rate, normalize_before, )) self.encoders = torch.nn.ModuleList(layers) def set_global_chunk_size(self, chunk_size): """Used in ONNX export. """ logging.info(f"set global chunk size: {chunk_size}, default is 0.") self.global_chunk_size = chunk_size if self.embed.subsampling_rate == 2: self.chunk_feature_map = 2 * self.global_chunk_size + 1 elif self.embed.subsampling_rate == 6: self.chunk_feature_map = 6 * self.global_chunk_size + 5 elif self.embed.subsampling_rate == 8: self.chunk_feature_map = 8 * self.global_chunk_size + 7 else: self.chunk_feature_map = 4 * self.global_chunk_size + 3 def output_size(self) -> int: return self._output_size def calculate_downsampling_factor(self, i: int) -> int: factor = 1 for idx, stride_idx in enumerate(self.stride_layer_idx): if i > stride_idx: factor *= self.stride[idx] return factor 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) """ 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) index = 0 # traverse stride for i, layer in enumerate(self.encoders): # layer return : x, mask, new_att_cache, new_cnn_cache xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) if i in self.stride_layer_idx: masks = masks[:, :, ::self.stride[index]] chunk_masks = chunk_masks[:, ::self.stride[index], ::self. stride[index]] mask_pad = masks pos_emb = pos_emb[:, ::self.stride[index], :] index = index + 1 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_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 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` att_mask : mask matrix of self attention Returns: torch.Tensor: output of current input xs torch.Tensor: subsampling cache required for next chunk computation List[torch.Tensor]: encoder layers output cache required for next chunk computation List[torch.Tensor]: conformer cnn cache """ assert xs.size(0) == 1 # using downsampling factor to recover offset offset *= self.calculate_downsampling_factor(self.num_blocks + 1) chunk_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) chunk_masks = chunk_masks.unsqueeze(1) # (1, 1, xs-time) real_len = 0 if self.global_chunk_size > 0: # for ONNX decode simulation， padding xs to chunk_size real_len = xs.size(1) pad_len = self.chunk_feature_map - real_len xs = F.pad(xs, (0, 0, 0, pad_len), value=0.0) chunk_masks = F.pad(chunk_masks, (0, pad_len), value=0.0) 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, chunk_masks = self.embed(xs, chunk_masks, offset) elayers, cache_t1 = att_cache.size(0), att_cache.size(2) chunk_size = xs.size(1) attention_key_size = cache_t1 + chunk_size # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim) # shape(pos_emb) = (b=1, chunk_size, emb_size=output_size=hidden-dim) 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 = [] mask_pad = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) mask_pad = mask_pad.unsqueeze(1) # batchPad (b=1, 1, time=chunk_size) if self.global_chunk_size > 0: # for ONNX decode simulation pos_emb = self.embed.position_encoding( offset=max(offset - cache_t1, 0), size=cache_t1 + self.global_chunk_size) att_mask[:, :, -self.global_chunk_size:] = chunk_masks mask_pad = chunk_masks.to(torch.bool) else: pos_emb = self.embed.position_encoding(offset=offset - cache_t1, size=attention_key_size) max_att_len, max_cnn_len = 0, 0 # for repeat_interleave of new_att_cache for i, layer in enumerate(self.encoders): factor = self.calculate_downsampling_factor(i) # 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) # shape(new_att_cache) = [ batch, head, time2, outdim//head * 2 ] att_cache_trunc = 0 if xs.size(1) + att_cache.size(2) / factor > pos_emb.size(1): # The time step is not divisible by the downsampling multiple att_cache_trunc = xs.size(1) + \ att_cache.size(2) // factor - pos_emb.size(1) + 1 xs, _, new_att_cache, new_cnn_cache = layer( xs, att_mask, pos_emb, mask_pad=mask_pad, att_cache=att_cache[i:i + 1, :, ::factor, :][:, :, att_cache_trunc:, :], cnn_cache=cnn_cache[i, :, :, :] if cnn_cache.size(0) > 0 else cnn_cache) if i in self.stride_layer_idx: # compute time dimension for next block efficient_index = self.stride_layer_idx.index(i) att_mask = att_mask[:, ::self.stride[efficient_index], ::self. stride[efficient_index]] mask_pad = mask_pad[:, ::self.stride[efficient_index], ::self. stride[efficient_index]] pos_emb = pos_emb[:, ::self.stride[efficient_index], :] # shape(new_att_cache) = [batch, head, time2, outdim] new_att_cache = new_att_cache[:, :, next_cache_start // factor:, :] # shape(new_cnn_cache) = [1, batch, outdim, cache_t2] new_cnn_cache = new_cnn_cache.unsqueeze(0) # use repeat_interleave to new_att_cache new_att_cache = new_att_cache.repeat_interleave(repeats=factor, dim=2) # padding new_cnn_cache to cnn.lorder for casual convolution new_cnn_cache = F.pad( new_cnn_cache, (self.cnn_module_kernel - 1 - new_cnn_cache.size(3), 0)) if i == 0: # record length for the first block as max length max_att_len = new_att_cache.size(2) max_cnn_len = new_cnn_cache.size(3) # update real shape of att_cache and cnn_cache r_att_cache.append(new_att_cache[:, :, -max_att_len:, :]) r_cnn_cache.append(new_cnn_cache[:, :, :, -max_cnn_len:]) 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) if self.global_chunk_size > 0 and real_len: chunk_real_len = real_len // self.embed.subsampling_rate // \ self.calculate_downsampling_factor(self.num_blocks + 1) # Keeping 1 more timestep can mitigate information leakage # from the encoder caused by the padding xs = xs[:, :chunk_real_len + 1, :] return xs, r_att_cache, r_cnn_cache def forward_chunk_by_chunk( self, xs: torch.Tensor, decoding_chunk_size: int, num_decoding_left_chunks: int = -1, use_onnx=False) -> 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) decoding_chunk_size (int): decoding chunk size num_decoding_left_chunks (int): use_onnx (bool): True for simulating ONNX model inference. """ 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) outputs = [] offset = 0 required_cache_size = decoding_chunk_size * num_decoding_left_chunks if use_onnx: logging.info("Simulating for ONNX runtime ...") att_cache: torch.Tensor = torch.zeros( (self.num_blocks, self.attention_heads, required_cache_size, self.output_size() // self.attention_heads * 2), device=xs.device) cnn_cache: torch.Tensor = torch.zeros( (self.num_blocks, 1, self.output_size(), self.cnn_module_kernel - 1), device=xs.device) self.set_global_chunk_size(chunk_size=decoding_chunk_size) else: logging.info("Simulating for JIT runtime ...") 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) # Feed forward overlap input step by step for cur in range(0, num_frames - context + 1, stride): end = min(cur + decoding_window, num_frames) logging.info(f"-->> frame chunk msg: cur={cur}, " f"end={end}, num_frames={end-cur}, " f"decoding_window={decoding_window}") if use_onnx: att_mask: torch.Tensor = torch.ones( (1, 1, required_cache_size + decoding_chunk_size), dtype=torch.bool, device=xs.device) if cur == 0: att_mask[:, :, :required_cache_size] = 0 else: att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool, device=xs.device) chunk_xs = xs[:, cur:end, :] (y, att_cache, cnn_cache) = \ self.forward_chunk( chunk_xs, offset, required_cache_size, att_cache, cnn_cache, att_mask) 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