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vggtransformer.py

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1# Copyright (c) Facebook, Inc. and its affiliates.2#3# This source code is licensed under the MIT license found in the4# LICENSE file in the root directory of this source tree.5import argparse6import math7from collections.abc import Iterable8import torch9import torch.nn as nn10from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask11from fairseq import utils12from fairseq.models import (13 FairseqEncoder,14 FairseqEncoderDecoderModel,15 FairseqEncoderModel,16 FairseqIncrementalDecoder,17 register_model,18 register_model_architecture,19)20from fairseq.modules import (21 LinearizedConvolution,22 TransformerDecoderLayer,23 TransformerEncoderLayer,24 VGGBlock,25)26@register_model("asr_vggtransformer")27class VGGTransformerModel(FairseqEncoderDecoderModel):28 """29 Transformers with convolutional context for ASR30 https://arxiv.org/abs/1904.1166031 """32 def __init__(self, encoder, decoder):33 super().__init__(encoder, decoder)34 @staticmethod35 def add_args(parser):36 """Add model-specific arguments to the parser."""37 parser.add_argument(38 "--input-feat-per-channel",39 type=int,40 metavar="N",41 help="encoder input dimension per input channel",42 )43 parser.add_argument(44 "--vggblock-enc-config",45 type=str,46 metavar="EXPR",47 help="""48 an array of tuples each containing the configuration of one vggblock:49 [(out_channels,50 conv_kernel_size,51 pooling_kernel_size,52 num_conv_layers,53 use_layer_norm), ...])54 """,55 )56 parser.add_argument(57 "--transformer-enc-config",58 type=str,59 metavar="EXPR",60 help=""""61 a tuple containing the configuration of the encoder transformer layers62 configurations:63 [(input_dim,64 num_heads,65 ffn_dim,66 normalize_before,67 dropout,68 attention_dropout,69 relu_dropout), ...]')70 """,71 )72 parser.add_argument(73 "--enc-output-dim",74 type=int,75 metavar="N",76 help="""77 encoder output dimension, can be None. If specified, projecting the78 transformer output to the specified dimension""",79 )80 parser.add_argument(81 "--in-channels",82 type=int,83 metavar="N",84 help="number of encoder input channels",85 )86 parser.add_argument(87 "--tgt-embed-dim",88 type=int,89 metavar="N",90 help="embedding dimension of the decoder target tokens",91 )92 parser.add_argument(93 "--transformer-dec-config",94 type=str,95 metavar="EXPR",96 help="""97 a tuple containing the configuration of the decoder transformer layers98 configurations:99 [(input_dim,100 num_heads,101 ffn_dim,102 normalize_before,103 dropout,104 attention_dropout,105 relu_dropout), ...]106 """,107 )108 parser.add_argument(109 "--conv-dec-config",110 type=str,111 metavar="EXPR",112 help="""113 an array of tuples for the decoder 1-D convolution config114 [(out_channels, conv_kernel_size, use_layer_norm), ...]""",115 )116 @classmethod117 def build_encoder(cls, args, task):118 return VGGTransformerEncoder(119 input_feat_per_channel=args.input_feat_per_channel,120 vggblock_config=eval(args.vggblock_enc_config),121 transformer_config=eval(args.transformer_enc_config),122 encoder_output_dim=args.enc_output_dim,123 in_channels=args.in_channels,124 )125 @classmethod126 def build_decoder(cls, args, task):127 return TransformerDecoder(128 dictionary=task.target_dictionary,129 embed_dim=args.tgt_embed_dim,130 transformer_config=eval(args.transformer_dec_config),131 conv_config=eval(args.conv_dec_config),132 encoder_output_dim=args.enc_output_dim,133 )134 @classmethod135 def build_model(cls, args, task):136 """Build a new model instance."""137 # make sure that all args are properly defaulted138 # (in case there are any new ones)139 base_architecture(args)140 encoder = cls.build_encoder(args, task)141 decoder = cls.build_decoder(args, task)142 return cls(encoder, decoder)143 def get_normalized_probs(self, net_output, log_probs, sample=None):144 # net_output['encoder_out'] is a (B, T, D) tensor145 lprobs = super().get_normalized_probs(net_output, log_probs, sample)146 lprobs.batch_first = True147 return lprobs148DEFAULT_ENC_VGGBLOCK_CONFIG = ((32, 3, 2, 2, False),) * 2149DEFAULT_ENC_TRANSFORMER_CONFIG = ((256, 4, 1024, True, 0.2, 0.2, 0.2),) * 2150# 256: embedding dimension151# 4: number of heads152# 1024: FFN153# True: apply layerNorm before (dropout + resiaul) instead of after154# 0.2 (dropout): dropout after MultiheadAttention and second FC155# 0.2 (attention_dropout): dropout in MultiheadAttention156# 0.2 (relu_dropout): dropout after ReLu157DEFAULT_DEC_TRANSFORMER_CONFIG = ((256, 2, 1024, True, 0.2, 0.2, 0.2),) * 2158DEFAULT_DEC_CONV_CONFIG = ((256, 3, True),) * 2159# TODO: repace transformer encoder config from one liner160# to explicit args to get rid of this transformation161def prepare_transformer_encoder_params(162 input_dim,163 num_heads,164 ffn_dim,165 normalize_before,166 dropout,167 attention_dropout,168 relu_dropout,169):170 args = argparse.Namespace()171 args.encoder_embed_dim = input_dim172 args.encoder_attention_heads = num_heads173 args.attention_dropout = attention_dropout174 args.dropout = dropout175 args.activation_dropout = relu_dropout176 args.encoder_normalize_before = normalize_before177 args.encoder_ffn_embed_dim = ffn_dim178 return args179def prepare_transformer_decoder_params(180 input_dim,181 num_heads,182 ffn_dim,183 normalize_before,184 dropout,185 attention_dropout,186 relu_dropout,187):188 args = argparse.Namespace()189 args.encoder_embed_dim = None190 args.decoder_embed_dim = input_dim191 args.decoder_attention_heads = num_heads192 args.attention_dropout = attention_dropout193 args.dropout = dropout194 args.activation_dropout = relu_dropout195 args.decoder_normalize_before = normalize_before196 args.decoder_ffn_embed_dim = ffn_dim197 return args198class VGGTransformerEncoder(FairseqEncoder):199 """VGG + Transformer encoder"""200 def __init__(201 self,202 input_feat_per_channel,203 vggblock_config=DEFAULT_ENC_VGGBLOCK_CONFIG,204 transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,205 encoder_output_dim=512,206 in_channels=1,207 transformer_context=None,208 transformer_sampling=None,209 ):210 """constructor for VGGTransformerEncoder211 Args:212 - input_feat_per_channel: feature dim (not including stacked,213 just base feature)214 - in_channel: # input channels (e.g., if stack 8 feature vector215 together, this is 8)216 - vggblock_config: configuration of vggblock, see comments on217 DEFAULT_ENC_VGGBLOCK_CONFIG218 - transformer_config: configuration of transformer layer, see comments219 on DEFAULT_ENC_TRANSFORMER_CONFIG220 - encoder_output_dim: final transformer output embedding dimension221 - transformer_context: (left, right) if set, self-attention will be focused222 on (t-left, t+right)223 - transformer_sampling: an iterable of int, must match with224 len(transformer_config), transformer_sampling[i] indicates sampling225 factor for i-th transformer layer, after multihead att and feedfoward226 part227 """228 super().__init__(None)229 self.num_vggblocks = 0230 if vggblock_config is not None:231 if not isinstance(vggblock_config, Iterable):232 raise ValueError("vggblock_config is not iterable")233 self.num_vggblocks = len(vggblock_config)234 self.conv_layers = nn.ModuleList()235 self.in_channels = in_channels236 self.input_dim = input_feat_per_channel237 self.pooling_kernel_sizes = []238 if vggblock_config is not None:239 for _, config in enumerate(vggblock_config):240 (241 out_channels,242 conv_kernel_size,243 pooling_kernel_size,244 num_conv_layers,245 layer_norm,246 ) = config247 self.conv_layers.append(248 VGGBlock(249 in_channels,250 out_channels,251 conv_kernel_size,252 pooling_kernel_size,253 num_conv_layers,254 input_dim=input_feat_per_channel,255 layer_norm=layer_norm,256 )257 )258 self.pooling_kernel_sizes.append(pooling_kernel_size)259 in_channels = out_channels260 input_feat_per_channel = self.conv_layers[-1].output_dim261 transformer_input_dim = self.infer_conv_output_dim(262 self.in_channels, self.input_dim263 )264 # transformer_input_dim is the output dimension of VGG part265 self.validate_transformer_config(transformer_config)266 self.transformer_context = self.parse_transformer_context(transformer_context)267 self.transformer_sampling = self.parse_transformer_sampling(268 transformer_sampling, len(transformer_config)269 )270 self.transformer_layers = nn.ModuleList()271 if transformer_input_dim != transformer_config[0][0]:272 self.transformer_layers.append(273 Linear(transformer_input_dim, transformer_config[0][0])274 )275 self.transformer_layers.append(276 TransformerEncoderLayer(277 prepare_transformer_encoder_params(*transformer_config[0])278 )279 )280 for i in range(1, len(transformer_config)):281 if transformer_config[i - 1][0] != transformer_config[i][0]:282 self.transformer_layers.append(283 Linear(transformer_config[i - 1][0], transformer_config[i][0])284 )285 self.transformer_layers.append(286 TransformerEncoderLayer(287 prepare_transformer_encoder_params(*transformer_config[i])288 )289 )290 self.encoder_output_dim = encoder_output_dim291 self.transformer_layers.extend(292 [293 Linear(transformer_config[-1][0], encoder_output_dim),294 LayerNorm(encoder_output_dim),295 ]296 )297 def forward(self, src_tokens, src_lengths, **kwargs):298 """299 src_tokens: padded tensor (B, T, C * feat)300 src_lengths: tensor of original lengths of input utterances (B,)301 """302 bsz, max_seq_len, _ = src_tokens.size()303 x = src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim)304 x = x.transpose(1, 2).contiguous()305 # (B, C, T, feat)306 for layer_idx in range(len(self.conv_layers)):307 x = self.conv_layers[layer_idx](x)308 bsz, _, output_seq_len, _ = x.size()309 # (B, C, T, feat) -> (B, T, C, feat) -> (T, B, C, feat) -> (T, B, C * feat)310 x = x.transpose(1, 2).transpose(0, 1)311 x = x.contiguous().view(output_seq_len, bsz, -1)312 input_lengths = src_lengths.clone()313 for s in self.pooling_kernel_sizes:314 input_lengths = (input_lengths.float() / s).ceil().long()315 encoder_padding_mask, _ = lengths_to_encoder_padding_mask(316 input_lengths, batch_first=True317 )318 if not encoder_padding_mask.any():319 encoder_padding_mask = None320 subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5)321 attn_mask = self.lengths_to_attn_mask(input_lengths, subsampling_factor)322 transformer_layer_idx = 0323 for layer_idx in range(len(self.transformer_layers)):324 if isinstance(self.transformer_layers[layer_idx], TransformerEncoderLayer):325 x = self.transformer_layers[layer_idx](326 x, encoder_padding_mask, attn_mask327 )328 if self.transformer_sampling[transformer_layer_idx] != 1:329 sampling_factor = self.transformer_sampling[transformer_layer_idx]330 x, encoder_padding_mask, attn_mask = self.slice(331 x, encoder_padding_mask, attn_mask, sampling_factor332 )333 transformer_layer_idx += 1334 else:335 x = self.transformer_layers[layer_idx](x)336 # encoder_padding_maks is a (T x B) tensor, its [t, b] elements indicate337 # whether encoder_output[t, b] is valid or not (valid=0, invalid=1)338 return {339 "encoder_out": x, # (T, B, C)340 "encoder_padding_mask": encoder_padding_mask.t()341 if encoder_padding_mask is not None342 else None,343 # (B, T) --> (T, B)344 }345 def infer_conv_output_dim(self, in_channels, input_dim):346 sample_seq_len = 200347 sample_bsz = 10348 x = torch.randn(sample_bsz, in_channels, sample_seq_len, input_dim)349 for i, _ in enumerate(self.conv_layers):350 x = self.conv_layers[i](x)351 x = x.transpose(1, 2)352 mb, seq = x.size()[:2]353 return x.contiguous().view(mb, seq, -1).size(-1)354 def validate_transformer_config(self, transformer_config):355 for config in transformer_config:356 input_dim, num_heads = config[:2]357 if input_dim % num_heads != 0:358 msg = (359 "ERROR in transformer config {}: ".format(config)360 + "input dimension {} ".format(input_dim)361 + "not dividable by number of heads {}".format(num_heads)362 )363 raise ValueError(msg)364 def parse_transformer_context(self, transformer_context):365 """366 transformer_context can be the following:367 - None; indicates no context is used, i.e.,368 transformer can access full context369 - a tuple/list of two int; indicates left and right context,370 any number <0 indicates infinite context371 * e.g., (5, 6) indicates that for query at x_t, transformer can372 access [t-5, t+6] (inclusive)373 * e.g., (-1, 6) indicates that for query at x_t, transformer can374 access [0, t+6] (inclusive)375 """376 if transformer_context is None:377 return None378 if not isinstance(transformer_context, Iterable):379 raise ValueError("transformer context must be Iterable if it is not None")380 if len(transformer_context) != 2:381 raise ValueError("transformer context must have length 2")382 left_context = transformer_context[0]383 if left_context < 0:384 left_context = None385 right_context = transformer_context[1]386 if right_context < 0:387 right_context = None388 if left_context is None and right_context is None:389 return None390 return (left_context, right_context)391 def parse_transformer_sampling(self, transformer_sampling, num_layers):392 """393 parsing transformer sampling configuration394 Args:395 - transformer_sampling, accepted input:396 * None, indicating no sampling397 * an Iterable with int (>0) as element398 - num_layers, expected number of transformer layers, must match with399 the length of transformer_sampling if it is not None400 Returns:401 - A tuple with length num_layers402 """403 if transformer_sampling is None:404 return (1,) * num_layers405 if not isinstance(transformer_sampling, Iterable):406 raise ValueError(407 "transformer_sampling must be an iterable if it is not None"408 )409 if len(transformer_sampling) != num_layers:410 raise ValueError(411 "transformer_sampling {} does not match with the number "412 "of layers {}".format(transformer_sampling, num_layers)413 )414 for layer, value in enumerate(transformer_sampling):415 if not isinstance(value, int):416 raise ValueError("Invalid value in transformer_sampling: ")417 if value < 1:418 raise ValueError(419 "{} layer's subsampling is {}.".format(layer, value)420 + " This is not allowed! "421 )422 return transformer_sampling423 def slice(self, embedding, padding_mask, attn_mask, sampling_factor):424 """425 embedding is a (T, B, D) tensor426 padding_mask is a (B, T) tensor or None427 attn_mask is a (T, T) tensor or None428 """429 embedding = embedding[::sampling_factor, :, :]430 if padding_mask is not None:431 padding_mask = padding_mask[:, ::sampling_factor]432 if attn_mask is not None:433 attn_mask = attn_mask[::sampling_factor, ::sampling_factor]434 return embedding, padding_mask, attn_mask435 def lengths_to_attn_mask(self, input_lengths, subsampling_factor=1):436 """437 create attention mask according to sequence lengths and transformer438 context439 Args:440 - input_lengths: (B, )-shape Int/Long tensor; input_lengths[b] is441 the length of b-th sequence442 - subsampling_factor: int443 * Note that the left_context and right_context is specified in444 the input frame-level while input to transformer may already445 go through subsampling (e.g., the use of striding in vggblock)446 we use subsampling_factor to scale the left/right context447 Return:448 - a (T, T) binary tensor or None, where T is max(input_lengths)449 * if self.transformer_context is None, None450 * if left_context is None,451 * attn_mask[t, t + right_context + 1:] = 1452 * others = 0453 * if right_context is None,454 * attn_mask[t, 0:t - left_context] = 1455 * others = 0456 * elsif457 * attn_mask[t, t - left_context: t + right_context + 1] = 0458 * others = 1459 """460 if self.transformer_context is None:461 return None462 maxT = torch.max(input_lengths).item()463 attn_mask = torch.zeros(maxT, maxT)464 left_context = self.transformer_context[0]465 right_context = self.transformer_context[1]466 if left_context is not None:467 left_context = math.ceil(self.transformer_context[0] / subsampling_factor)468 if right_context is not None:469 right_context = math.ceil(self.transformer_context[1] / subsampling_factor)470 for t in range(maxT):471 if left_context is not None:472 st = 0473 en = max(st, t - left_context)474 attn_mask[t, st:en] = 1475 if right_context is not None:476 st = t + right_context + 1477 st = min(st, maxT - 1)478 attn_mask[t, st:] = 1479 return attn_mask.to(input_lengths.device)480 def reorder_encoder_out(self, encoder_out, new_order):481 encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(482 1, new_order483 )484 if encoder_out["encoder_padding_mask"] is not None:485 encoder_out["encoder_padding_mask"] = encoder_out[486 "encoder_padding_mask"487 ].index_select(1, new_order)488 return encoder_out489class TransformerDecoder(FairseqIncrementalDecoder):490 """491 Transformer decoder consisting of *args.decoder_layers* layers. Each layer492 is a :class:`TransformerDecoderLayer`.493 Args:494 args (argparse.Namespace): parsed command-line arguments495 dictionary (~fairseq.data.Dictionary): decoding dictionary496 embed_tokens (torch.nn.Embedding): output embedding497 no_encoder_attn (bool, optional): whether to attend to encoder outputs.498 Default: ``False``499 left_pad (bool, optional): whether the input is left-padded. Default:500 ``False``501 """502 def __init__(503 self,504 dictionary,505 embed_dim=512,506 transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,507 conv_config=DEFAULT_DEC_CONV_CONFIG,508 encoder_output_dim=512,509 ):510 super().__init__(dictionary)511 vocab_size = len(dictionary)512 self.padding_idx = dictionary.pad()513 self.embed_tokens = Embedding(vocab_size, embed_dim, self.padding_idx)514 self.conv_layers = nn.ModuleList()515 for i in range(len(conv_config)):516 out_channels, kernel_size, layer_norm = conv_config[i]517 if i == 0:518 conv_layer = LinearizedConv1d(519 embed_dim, out_channels, kernel_size, padding=kernel_size - 1520 )521 else:522 conv_layer = LinearizedConv1d(523 conv_config[i - 1][0],524 out_channels,525 kernel_size,526 padding=kernel_size - 1,527 )528 self.conv_layers.append(conv_layer)529 if layer_norm:530 self.conv_layers.append(nn.LayerNorm(out_channels))531 self.conv_layers.append(nn.ReLU())532 self.layers = nn.ModuleList()533 if conv_config[-1][0] != transformer_config[0][0]:534 self.layers.append(Linear(conv_config[-1][0], transformer_config[0][0]))535 self.layers.append(536 TransformerDecoderLayer(537 prepare_transformer_decoder_params(*transformer_config[0])538 )539 )540 for i in range(1, len(transformer_config)):541 if transformer_config[i - 1][0] != transformer_config[i][0]:542 self.layers.append(543 Linear(transformer_config[i - 1][0], transformer_config[i][0])544 )545 self.layers.append(546 TransformerDecoderLayer(547 prepare_transformer_decoder_params(*transformer_config[i])548 )549 )550 self.fc_out = Linear(transformer_config[-1][0], vocab_size)551 def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None):552 """553 Args:554 prev_output_tokens (LongTensor): previous decoder outputs of shape555 `(batch, tgt_len)`, for input feeding/teacher forcing556 encoder_out (Tensor, optional): output from the encoder, used for557 encoder-side attention558 incremental_state (dict): dictionary used for storing state during559 :ref:`Incremental decoding`560 Returns:561 tuple:562 - the last decoder layer's output of shape `(batch, tgt_len,563 vocab)`564 - the last decoder layer's attention weights of shape `(batch,565 tgt_len, src_len)`566 """567 target_padding_mask = (568 (prev_output_tokens == self.padding_idx).to(prev_output_tokens.device)569 if incremental_state is None570 else None571 )572 if incremental_state is not None:573 prev_output_tokens = prev_output_tokens[:, -1:]574 # embed tokens575 x = self.embed_tokens(prev_output_tokens)576 # B x T x C -> T x B x C577 x = self._transpose_if_training(x, incremental_state)578 for layer in self.conv_layers:579 if isinstance(layer, LinearizedConvolution):580 x = layer(x, incremental_state)581 else:582 x = layer(x)583 # B x T x C -> T x B x C584 x = self._transpose_if_inference(x, incremental_state)585 # decoder layers586 for layer in self.layers:587 if isinstance(layer, TransformerDecoderLayer):588 x, *_ = layer(589 x,590 (encoder_out["encoder_out"] if encoder_out is not None else None),591 (592 encoder_out["encoder_padding_mask"].t()593 if encoder_out["encoder_padding_mask"] is not None594 else None595 ),596 incremental_state,597 self_attn_mask=(598 self.buffered_future_mask(x)599 if incremental_state is None600 else None601 ),602 self_attn_padding_mask=(603 target_padding_mask if incremental_state is None else None604 ),605 )606 else:607 x = layer(x)608 # T x B x C -> B x T x C609 x = x.transpose(0, 1)610 x = self.fc_out(x)611 return x, None612 def buffered_future_mask(self, tensor):613 dim = tensor.size(0)614 if (615 not hasattr(self, "_future_mask")616 or self._future_mask is None617 or self._future_mask.device != tensor.device618 ):619 self._future_mask = torch.triu(620 utils.fill_with_neg_inf(tensor.new(dim, dim)), 1621 )622 if self._future_mask.size(0) < dim:623 self._future_mask = torch.triu(624 utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1625 )626 return self._future_mask[:dim, :dim]627 def _transpose_if_training(self, x, incremental_state):628 if incremental_state is None:629 x = x.transpose(0, 1)630 return x631 def _transpose_if_inference(self, x, incremental_state):632 if incremental_state:633 x = x.transpose(0, 1)634 return x635@register_model("asr_vggtransformer_encoder")636class VGGTransformerEncoderModel(FairseqEncoderModel):637 def __init__(self, encoder):638 super().__init__(encoder)639 @staticmethod640 def add_args(parser):641 """Add model-specific arguments to the parser."""642 parser.add_argument(643 "--input-feat-per-channel",644 type=int,645 metavar="N",646 help="encoder input dimension per input channel",647 )648 parser.add_argument(649 "--vggblock-enc-config",650 type=str,651 metavar="EXPR",652 help="""653 an array of tuples each containing the configuration of one vggblock654 [(out_channels, conv_kernel_size, pooling_kernel_size,num_conv_layers), ...]655 """,656 )657 parser.add_argument(658 "--transformer-enc-config",659 type=str,660 metavar="EXPR",661 help="""662 a tuple containing the configuration of the Transformer layers663 configurations:664 [(input_dim,665 num_heads,666 ffn_dim,667 normalize_before,668 dropout,669 attention_dropout,670 relu_dropout), ]""",671 )672 parser.add_argument(673 "--enc-output-dim",674 type=int,675 metavar="N",676 help="encoder output dimension, projecting the LSTM output",677 )678 parser.add_argument(679 "--in-channels",680 type=int,681 metavar="N",682 help="number of encoder input channels",683 )684 parser.add_argument(685 "--transformer-context",686 type=str,687 metavar="EXPR",688 help="""689 either None or a tuple of two ints, indicating left/right context a690 transformer can have access to""",691 )692 parser.add_argument(693 "--transformer-sampling",694 type=str,695 metavar="EXPR",696 help="""697 either None or a tuple of ints, indicating sampling factor in each layer""",698 )699 @classmethod700 def build_model(cls, args, task):701 """Build a new model instance."""702 base_architecture_enconly(args)703 encoder = VGGTransformerEncoderOnly(704 vocab_size=len(task.target_dictionary),705 input_feat_per_channel=args.input_feat_per_channel,706 vggblock_config=eval(args.vggblock_enc_config),707 transformer_config=eval(args.transformer_enc_config),708 encoder_output_dim=args.enc_output_dim,709 in_channels=args.in_channels,710 transformer_context=eval(args.transformer_context),711 transformer_sampling=eval(args.transformer_sampling),712 )713 return cls(encoder)714 def get_normalized_probs(self, net_output, log_probs, sample=None):715 # net_output['encoder_out'] is a (T, B, D) tensor716 lprobs = super().get_normalized_probs(net_output, log_probs, sample)717 # lprobs is a (T, B, D) tensor718 # we need to transoose to get (B, T, D) tensor719 lprobs = lprobs.transpose(0, 1).contiguous()720 lprobs.batch_first = True721 return lprobs722class VGGTransformerEncoderOnly(VGGTransformerEncoder):723 def __init__(724 self,725 vocab_size,726 input_feat_per_channel,727 vggblock_config=DEFAULT_ENC_VGGBLOCK_CONFIG,728 transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,729 encoder_output_dim=512,730 in_channels=1,731 transformer_context=None,732 transformer_sampling=None,733 ):734 super().__init__(735 input_feat_per_channel=input_feat_per_channel,736 vggblock_config=vggblock_config,737 transformer_config=transformer_config,738 encoder_output_dim=encoder_output_dim,739 in_channels=in_channels,740 transformer_context=transformer_context,741 transformer_sampling=transformer_sampling,742 )743 self.fc_out = Linear(self.encoder_output_dim, vocab_size)744 def forward(self, src_tokens, src_lengths, **kwargs):745 """746 src_tokens: padded tensor (B, T, C * feat)747 src_lengths: tensor of original lengths of input utterances (B,)748 """749 enc_out = super().forward(src_tokens, src_lengths)750 x = self.fc_out(enc_out["encoder_out"])751 # x = F.log_softmax(x, dim=-1)752 # Note: no need this line, because model.get_normalized_prob will call753 # log_softmax754 return {755 "encoder_out": x, # (T, B, C)756 "encoder_padding_mask": enc_out["encoder_padding_mask"], # (T, B)757 }758 def max_positions(self):759 """Maximum input length supported by the encoder."""760 return (1e6, 1e6) # an arbitrary large number761def Embedding(num_embeddings, embedding_dim, padding_idx):762 m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)763 # nn.init.uniform_(m.weight, -0.1, 0.1)764 # nn.init.constant_(m.weight[padding_idx], 0)765 return m766def Linear(in_features, out_features, bias=True, dropout=0):767 """Linear layer (input: N x T x C)"""768 m = nn.Linear(in_features, out_features, bias=bias)769 # m.weight.data.uniform_(-0.1, 0.1)770 # if bias:771 # m.bias.data.uniform_(-0.1, 0.1)772 return m773def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs):774 """Weight-normalized Conv1d layer optimized for decoding"""775 m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)776 std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))777 nn.init.normal_(m.weight, mean=0, std=std)778 nn.init.constant_(m.bias, 0)779 return nn.utils.weight_norm(m, dim=2)780def LayerNorm(embedding_dim):781 m = nn.LayerNorm(embedding_dim)782 return m783# seq2seq models784def base_architecture(args):785 args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 40)786 args.vggblock_enc_config = getattr(787 args, "vggblock_enc_config", DEFAULT_ENC_VGGBLOCK_CONFIG788 )789 args.transformer_enc_config = getattr(790 args, "transformer_enc_config", DEFAULT_ENC_TRANSFORMER_CONFIG791 )792 args.enc_output_dim = getattr(args, "enc_output_dim", 512)793 args.in_channels = getattr(args, "in_channels", 1)794 args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 128)795 args.transformer_dec_config = getattr(796 args, "transformer_dec_config", DEFAULT_ENC_TRANSFORMER_CONFIG797 )798 args.conv_dec_config = getattr(args, "conv_dec_config", DEFAULT_DEC_CONV_CONFIG)799 args.transformer_context = getattr(args, "transformer_context", "None")800@register_model_architecture("asr_vggtransformer", "vggtransformer_1")801def vggtransformer_1(args):802 args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)803 args.vggblock_enc_config = getattr(804 args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"805 )806 args.transformer_enc_config = getattr(807 args,808 "transformer_enc_config",809 "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 14",810 )811 args.enc_output_dim = getattr(args, "enc_output_dim", 1024)812 args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 128)813 args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4")814 args.transformer_dec_config = getattr(815 args,816 "transformer_dec_config",817 "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 4",818 )819@register_model_architecture("asr_vggtransformer", "vggtransformer_2")820def vggtransformer_2(args):821 args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)822 args.vggblock_enc_config = getattr(823 args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"824 )825 args.transformer_enc_config = getattr(826 args,827 "transformer_enc_config",828 "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 16",829 )830 args.enc_output_dim = getattr(args, "enc_output_dim", 1024)831 args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 512)832 args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4")833 args.transformer_dec_config = getattr(834 args,835 "transformer_dec_config",836 "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 6",837 )838@register_model_architecture("asr_vggtransformer", "vggtransformer_base")839def vggtransformer_base(args):840 args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)841 args.vggblock_enc_config = getattr(842 args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"843 )844 args.transformer_enc_config = getattr(845 args, "transformer_enc_config", "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 12"846 )847 args.enc_output_dim = getattr(args, "enc_output_dim", 512)848 args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 512)849 args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4")850 args.transformer_dec_config = getattr(851 args, "transformer_dec_config", "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 6"852 )853 # Size estimations:854 # Encoder:855 # - vggblock param: 64*1*3*3 + 64*64*3*3 + 128*64*3*3 + 128*128*3 = 258K856 # Transformer:857 # - input dimension adapter: 2560 x 512 -> 1.31M858 # - transformer_layers (x12) --> 37.74M859 # * MultiheadAttention: 512*512*3 (in_proj) + 512*512 (out_proj) = 1.048M860 # * FFN weight: 512*2048*2 = 2.097M861 # - output dimension adapter: 512 x 512 -> 0.26 M862 # Decoder:863 # - LinearizedConv1d: 512 * 256 * 3 + 256 * 256 * 3 * 3864 # - transformer_layer: (x6) --> 25.16M865 # * MultiheadAttention (self-attention): 512*512*3 + 512*512 = 1.048M866 # * MultiheadAttention (encoder-attention): 512*512*3 + 512*512 = 1.048M867 # * FFN: 512*2048*2 = 2.097M868 # Final FC:869 # - FC: 512*5000 = 256K (assuming vocab size 5K)870 # In total:871 # ~65 M872# CTC models873def base_architecture_enconly(args):874 args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 40)875 args.vggblock_enc_config = getattr(876 args, "vggblock_enc_config", "[(32, 3, 2, 2, True)] * 2"877 )878 args.transformer_enc_config = getattr(879 args, "transformer_enc_config", "((256, 4, 1024, True, 0.2, 0.2, 0.2),) * 2"880 )881 args.enc_output_dim = getattr(args, "enc_output_dim", 512)882 args.in_channels = getattr(args, "in_channels", 1)883 args.transformer_context = getattr(args, "transformer_context", "None")884 args.transformer_sampling = getattr(args, "transformer_sampling", "None")885@register_model_architecture("asr_vggtransformer_encoder", "vggtransformer_enc_1")886def vggtransformer_enc_1(args):887 # vggtransformer_1 is the same as vggtransformer_enc_big, except the number888 # of layers is increased to 16889 # keep it here for backward compatiablity purpose890 args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)891 args.vggblock_enc_config = getattr(892 args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"893 )894 args.transformer_enc_config = getattr(895 args,896 "transformer_enc_config",897 "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 16",898 )...

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FromOptionsTransformer.js

Source:FromOptionsTransformer.js Github

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...59 constructor(reporter, idGenerator = new UniqueIdentifierGenerator()) {60 super(reporter, options.validate);61 var append = (transformer) => {62 this.append((tree) => {63 return new transformer(idGenerator, reporter).transformAny(tree);64 });65 };66 if (transformOptions.blockBinding) {67 this.append((tree) => {68 validateConst(tree, reporter);69 return tree;70 });71 }72 // Issue errors for any unbound variables73 if (options.freeVariableChecker) {74 this.append((tree) => {75 validateFreeVariables(tree, reporter);76 return tree;77 });...

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QueryBuilderTest.js

Source:QueryBuilderTest.js Github

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1/*global ngDescribe, describe, it, expect, angular*/2ngDescribe({3 name: 'QueryBuilder',4 modules: 'lucidworksView.utils.queryBuilder',5 inject: ['QueryBuilder'],6 tests: function (deps) {7 describe('urls should be created from objects.', function(){8 it('should turn a flat object into a string', function(){9 var obj = {q: 'test', start: 0, rows: 10, wt: 'json'};10 expect(deps.QueryBuilder.objectToURLString(obj)).toEqual('q=test&start=0&rows=10&wt=json');11 });12 it('should turn hash tags into uri encoded components', function(){13 var obj = {q: '#1', start: 0, rows: 10, wt: 'json'};14 expect(deps.QueryBuilder.objectToURLString(obj)).toEqual('q=%231&start=0&rows=10&wt=json');15 obj = {q: '1#2#3', start: 0, rows: 10, wt: 'json'};16 expect(deps.QueryBuilder.objectToURLString(obj)).toEqual('q=1%232%233&start=0&rows=10&wt=json');17 });18 });19 describe('transformers should be registered', function() {20 it('should be able to register keyValue transformers', function(){21 deps.QueryBuilder.registerTransformer('keyValue', 'test', keyValueTransformerFunc);22 function keyValueTransformerFunc(key, value){23 return key +'!!!'+ value;24 }25 var obj = {q: '#1', start: 0, rows: 10, wt: 'json', fq: [{key: 'department', transformer: 'test', values: ['VIDEO/COMPACT DISC']}]};26 expect(deps.QueryBuilder.objectToURLString(obj)).toEqual('q=%231&start=0&rows=10&wt=json&fq=department!!!VIDEO/COMPACT DISC');27 });28 it('should be able to register preEncodeWrapper transformers', function(){29 deps.QueryBuilder.registerTransformer('preEncodeWrapper', 'test', preEncodeWrapperTransformerFunc);30 function preEncodeWrapperTransformerFunc(data){31 return '!!!'+data+'!!!';32 }33 var obj = {q: '#1', start: 0, rows: 10, wt: 'json', fq: [{key: 'department', transformer: 'test', values: ['VIDEO/COMPACT DISC']}]};34 expect(deps.QueryBuilder.objectToURLString(obj)).toEqual('q=%231&start=0&rows=10&wt=json&fq=department=!!!VIDEO/COMPACT DISC!!!');35 });36 it('should be able to register encode transformers', function(){37 deps.QueryBuilder.registerTransformer('preEncodeWrapper', 'test', preEncodeWrapperTransformerFunc);38 deps.QueryBuilder.registerTransformer('encode', 'test', encodeTransformerFunc);39 function preEncodeWrapperTransformerFunc(data){40 return '!!!'+data+'!!!';41 }42 function encodeTransformerFunc(data){43 return 'XXX'+data+'XXX';44 }45 var obj = {q: '#1', start: 0, rows: 10, wt: 'json', fq: [{key: 'department', transformer: 'test', values: ['VIDEO/COMPACT DISC']}]};46 expect(deps.QueryBuilder.objectToURLString(obj)).toEqual('q=%231&start=0&rows=10&wt=json&fq=department=XXX!!!VIDEO/COMPACT DISC!!!XXX');47 });48 it('should be able to register wrapper transformers', function(){49 deps.QueryBuilder.registerTransformer('preEncodeWrapper', 'test', preEncodeWrapperTransformerFunc);50 deps.QueryBuilder.registerTransformer('encode', 'test', encodeTransformerFunc);51 deps.QueryBuilder.registerTransformer('wrapper', 'test', wrapperTransformerFunc);52 function preEncodeWrapperTransformerFunc(data){53 return '!!!'+data+'!!!';54 }55 function encodeTransformerFunc(data){56 return 'XXX'+data+'XXX';57 }58 function wrapperTransformerFunc(data){59 return '['+data+']';60 }61 var obj = {q: '#1', start: 0, rows: 10, wt: 'json', fq: [{key: 'department', transformer: 'test', values: ['VIDEO/COMPACT DISC']}]};62 expect(deps.QueryBuilder.objectToURLString(obj)).toEqual('q=%231&start=0&rows=10&wt=json&fq=department=[XXX!!!VIDEO/COMPACT DISC!!!XXX]');63 });64 it('should be able to register join transformers', function(){65 deps.QueryBuilder.registerTransformer('preEncodeWrapper', 'test', preEncodeWrapperTransformerFunc);66 deps.QueryBuilder.registerTransformer('encode', 'test', encodeTransformerFunc);67 deps.QueryBuilder.registerTransformer('wrapper', 'test', wrapperTransformerFunc);68 deps.QueryBuilder.registerTransformer('join', 'test', joinTransformerFunc);69 function preEncodeWrapperTransformerFunc(data){70 return '!!!'+data+'!!!';71 }72 function encodeTransformerFunc(data){73 return 'XXX'+data+'XXX';74 }75 function wrapperTransformerFunc(data){76 return '['+data+']';77 }78 function joinTransformerFunc(str, arr){79 var join = '~';80 if(angular.isString(arr)){81 return str + join + arr;82 }83 return _.reduce(arr, arrayJoinStringReducer, str);84 function arrayJoinStringReducer(str, value){85 return str + ((str!=='')?join:'') + value;86 }87 }88 var obj = {q: '#1', start: 0, rows: 10, wt: 'json', fq: [{key: 'department', transformer: 'test', values: ['VIDEO/COMPACT DISC', 'LASERCAT']}]};89 expect(deps.QueryBuilder.objectToURLString(obj)).toEqual('q=%231&start=0&rows=10&wt=json&fq=department=[XXX!!!VIDEO/COMPACT DISC!!!XXX]~department=[XXX!!!LASERCAT!!!XXX]');90 });91 });92 describe('helper functions should help transform', function(){93 it('should escape special characters used in lucene', function(){94 expect(deps.QueryBuilder.escapeSpecialChars('string + - && { } has special chars ? * ||')).toEqual('string \\+ \\- \\\&\\\& \\{ \\} has special chars \\? \\* \\|\\|');95 });96 it('should be able to encode URI components with plusses', function(){97 expect(deps.QueryBuilder.encodeURIComponentPlus('string % $ has spaces')).toEqual('string+%25+%24+has+spaces');98 });99 it('should help build key value string', function(){100 expect(deps.QueryBuilder.keyValueString('key', 'value', 'join')).toEqual('keyjoinvalue');101 });102 it('should help build an array joined string', function(){103 expect(deps.QueryBuilder.arrayJoinString('', ['in','the','array'], '~')).toEqual('in~the~array');104 expect(deps.QueryBuilder.arrayJoinString('thisIsAString', ['in','the','array'], '~')).toEqual('thisIsAString~in~the~array');105 });106 });107 }...

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ConvertingTransformersModule.ts

Source:ConvertingTransformersModule.ts Github

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1import { ContainerModule, interfaces } from 'inversify';2import { InversifyContainerFacade } from '../../InversifyContainerFacade';3import { ServiceIdentifiers } from '../../ServiceIdentifiers';4import { INodeTransformer } from '../../../interfaces/node-transformers/INodeTransformer';5import { IObjectExpressionExtractor } from '../../../interfaces/node-transformers/converting-transformers/object-expression-extractors/IObjectExpressionExtractor';6import { NodeTransformer } from '../../../enums/node-transformers/NodeTransformer';7import { ObjectExpressionExtractor } from '../../../enums/node-transformers/converting-transformers/properties-extractors/ObjectExpressionExtractor';8import { BasePropertiesExtractor } from '../../../node-transformers/converting-transformers/object-expression-extractors/BasePropertiesExtractor';9import { BooleanLiteralTransformer } from '../../../node-transformers/converting-transformers/BooleanLiteralTransformer';10import { ExportSpecifierTransformer } from '../../../node-transformers/converting-transformers/ExportSpecifierTransformer';11import { MemberExpressionTransformer } from '../../../node-transformers/converting-transformers/MemberExpressionTransformer';12import { ClassFieldTransformer } from '../../../node-transformers/converting-transformers/ClassFieldTransformer';13import { NumberLiteralTransformer } from '../../../node-transformers/converting-transformers/NumberLiteralTransformer';14import { NumberToNumericalExpressionTransformer } from '../../../node-transformers/converting-transformers/NumberToNumericalExpressionTransformer';15import { ObjectExpressionKeysTransformer } from '../../../node-transformers/converting-transformers/ObjectExpressionKeysTransformer';16import { ObjectExpressionToVariableDeclarationExtractor } from '../../../node-transformers/converting-transformers/object-expression-extractors/ObjectExpressionToVariableDeclarationExtractor';17import { ObjectExpressionTransformer } from '../../../node-transformers/converting-transformers/ObjectExpressionTransformer';18import { ObjectPatternPropertiesTransformer } from '../../../node-transformers/converting-transformers/ObjectPatternPropertiesTransformer';19import { SplitStringTransformer } from '../../../node-transformers/converting-transformers/SplitStringTransformer';20import { TemplateLiteralTransformer } from '../../../node-transformers/converting-transformers/TemplateLiteralTransformer';21export const convertingTransformersModule: interfaces.ContainerModule = new ContainerModule((bind: interfaces.Bind) => {22 // converting transformers23 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)24 .to(BooleanLiteralTransformer)25 .whenTargetNamed(NodeTransformer.BooleanLiteralTransformer);26 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)27 .to(ExportSpecifierTransformer)28 .whenTargetNamed(NodeTransformer.ExportSpecifierTransformer);29 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)30 .to(MemberExpressionTransformer)31 .whenTargetNamed(NodeTransformer.MemberExpressionTransformer);32 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)33 .to(ClassFieldTransformer)34 .whenTargetNamed(NodeTransformer.ClassFieldTransformer);35 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)36 .to(NumberLiteralTransformer)37 .whenTargetNamed(NodeTransformer.NumberLiteralTransformer);38 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)39 .to(NumberToNumericalExpressionTransformer)40 .whenTargetNamed(NodeTransformer.NumberToNumericalExpressionTransformer);41 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)42 .to(ObjectExpressionKeysTransformer)43 .whenTargetNamed(NodeTransformer.ObjectExpressionKeysTransformer);44 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)45 .to(ObjectExpressionTransformer)46 .whenTargetNamed(NodeTransformer.ObjectExpressionTransformer);47 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)48 .to(ObjectPatternPropertiesTransformer)49 .whenTargetNamed(NodeTransformer.ObjectPatternPropertiesTransformer);50 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)51 .to(SplitStringTransformer)52 .whenTargetNamed(NodeTransformer.SplitStringTransformer);53 bind<INodeTransformer>(ServiceIdentifiers.INodeTransformer)54 .to(TemplateLiteralTransformer)55 .whenTargetNamed(NodeTransformer.TemplateLiteralTransformer);56 // object expression extractors57 bind<IObjectExpressionExtractor>(ServiceIdentifiers.IObjectExpressionExtractor)58 .to(ObjectExpressionToVariableDeclarationExtractor)59 .whenTargetNamed(ObjectExpressionExtractor.ObjectExpressionToVariableDeclarationExtractor);60 bind<IObjectExpressionExtractor>(ServiceIdentifiers.IObjectExpressionExtractor)61 .to(BasePropertiesExtractor)62 .whenTargetNamed(ObjectExpressionExtractor.BasePropertiesExtractor);63 // object expression extractor factory64 bind<IObjectExpressionExtractor>(ServiceIdentifiers.Factory__IObjectExpressionExtractor)65 .toFactory<IObjectExpressionExtractor>(InversifyContainerFacade66 .getCacheFactory<ObjectExpressionExtractor, IObjectExpressionExtractor>(67 ServiceIdentifiers.IObjectExpressionExtractor68 ));...

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DefaultValueTransformers.ts

Source:DefaultValueTransformers.ts Github

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1/* eslint-disable @typescript-eslint/explicit-module-boundary-types */2/* eslint-disable @typescript-eslint/no-explicit-any */3import { ValueTransformer } from './ValueTransformer';4export class JsonTransformer implements ValueTransformer {5 toDB(input: any): string | null {6 return input == undefined ? null : JSON.stringify(input);7 }8 fromDB(input: string | null): any {9 return input == null ? undefined : JSON.parse(input);10 }11}12export class BooleanTextTransformer implements ValueTransformer {13 toDB(input: boolean | undefined): string | null {14 return input == undefined ? null : !input ? '0' : '1';15 }16 fromDB(input: string | null): boolean | undefined {17 if (input == null) {18 return undefined;19 }20 if (input === '0' || input === 'false') {21 return false;22 } else if (input === '1' || input === 'true') {23 return true;24 }25 return undefined;26 }27}28export class BooleanNumberTransformer implements ValueTransformer {29 toDB(input: boolean | undefined): number | null {30 return input == undefined ? null : !input ? 0 : 1;31 }32 fromDB(input: number | null): boolean | undefined {33 return input == null ? undefined : !input ? false : true;34 }35}36export class DateTextTransformer implements ValueTransformer {37 toDB(input: Date | undefined): string | null {38 return input == undefined ? null : input.toISOString();39 }40 fromDB(input: string | null): Date | undefined {41 return input == null ? undefined : new Date(Date.parse(input));42 }43}44export class DateIntegerAsSecondsTransformer implements ValueTransformer {45 toDB(input: Date | undefined): number | null {46 return input == undefined ? null : Math.floor(input.getTime() / 1000);47 }48 fromDB(input: number | null): Date | undefined {49 return input == null ? undefined : new Date(Number.isInteger(input) ? input * 1000 : NaN);50 }51}52export class DateIntegerAsMillisecondsTransformer implements ValueTransformer {53 toDB(input: Date | undefined): number | null {54 return input == undefined ? null : input.getTime();55 }56 fromDB(input: number | null): Date | undefined {57 return input == null ? undefined : new Date(Number.isInteger(input) ? input : NaN);58 }59}60export class NumberTextTransformer implements ValueTransformer {61 toDB(input: number | undefined): string | null {62 return input == undefined ? null : String(input);63 }64 fromDB(input: string | null): number | undefined {65 return input == null ? undefined : Number(input);66 }67}68export class NumberDefaultTransformer implements ValueTransformer {69 toDB(input: number | undefined): number | null {70 return input == undefined ? null : Number(input);71 }72 fromDB(input: number | null): number | undefined {73 return input == null ? undefined : Number(input);74 }75}76export class StringDefaultTransformer implements ValueTransformer {77 toDB(input: string | undefined): string | null {78 return input == undefined ? null : String(input);79 }80 fromDB(input: string | null): string | undefined {81 return input == null ? undefined : String(input);82 }83}84export class StringNumberTransformer implements ValueTransformer {85 toDB(input: string | undefined): number | null {86 return input == undefined ? null : Number(input);87 }88 fromDB(input: number | null): string | undefined {89 return input == null ? undefined : String(input);90 }91}92export class UnknownDefaultTransformer implements ValueTransformer {93 /* istanbul ignore next */94 toDB(input: any | undefined): string | null {95 return input == undefined ? null : input;96 }97 /* istanbul ignore next */98 fromDB(input: string | null): string | undefined {99 return input == null ? undefined : input;100 }101}102export class DefaultValueTransformers {103 readonly json: ValueTransformer = new JsonTransformer();104 readonly booleanText: ValueTransformer = new BooleanTextTransformer();105 readonly booleanNumber: ValueTransformer = new BooleanNumberTransformer();106 readonly dateText: ValueTransformer = new DateTextTransformer();107 readonly dateIntegerAsSeconds: ValueTransformer = new DateIntegerAsSecondsTransformer();108 readonly dateIntegerAsMilliseconds: ValueTransformer = new DateIntegerAsMillisecondsTransformer();109 readonly numberText: ValueTransformer = new NumberTextTransformer();110 readonly numberDefault: ValueTransformer = new NumberDefaultTransformer();111 readonly stringNumber: ValueTransformer = new StringNumberTransformer();112 readonly stringDefault: ValueTransformer = new StringDefaultTransformer();113 readonly unknownDefault: ValueTransformer = new UnknownDefaultTransformer();114}...

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index.ts

Source:index.ts Github

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1import { callStatementTransformer } from "./call-statement";2import { doWhileStatementTransformer } from "./do-while-statement";3import { forOfStatementTransformer } from "./for-of-statement";4import { ifStatementTransformer } from "./if-statement";5import { promiseAllStatementTransformer } from "./promise-all-statement";6import { removeUnnecessaryExpressionsTransformer } from "./remove-unneccesary-expressionts";7import { switchStatementTransformer } from "./switch-statement";8import { throwStatementTransformer } from "./throw-statement";9import { tryStatementTransformer } from "./try-statement";10import { unsupportedStatementTransformer } from "./unsupported";11import { whileStatementTransformer } from "./while-statement";12import { consoleLogStatementTransformer } from "./log-statement";13import { arrayFilterTransformer } from "./array-filter-statement";14import { arrayMapTransformer } from "./array-map-statement";15import { arrayLengthTransformer } from "./array-length-statement";16import { ConverterOptions } from "../../convert";17import { resolveExpressionsTransformer } from "./array-initializer";18import { stringTemplateTransformer } from "./string-template";19import { literalExpressionTransformer } from "./resolve-literal-expressions";20import { deployTimeStatementTransformer } from "./deploy-time-replacements";21import { stringConversionTransformer } from "./string-conversion";22import { ICompilerHost } from "../../compiler-host";23import { evalConstTransformer } from "./eval-const";24import { enumValueTransformer } from "./enum-values";25import { nullCoalescingStatementTransformer } from "./null-coalescing-statement";26import { newDateTransformer } from "./datetime-stmt";27export const createTransformers = (converterOptions: ConverterOptions = {}, host: ICompilerHost) => {28 return [29 evalConstTransformer(host.typeChecker),30 enumValueTransformer(host.typeChecker),31 removeUnnecessaryExpressionsTransformer(converterOptions),32 unsupportedStatementTransformer(converterOptions),33 ifStatementTransformer(converterOptions),34 arrayFilterTransformer(converterOptions),35 arrayMapTransformer(converterOptions),36 arrayLengthTransformer(converterOptions),37 switchStatementTransformer(converterOptions),38 tryStatementTransformer(converterOptions),39 resolveExpressionsTransformer(converterOptions),40 //variableStatementTransformer(converterOptions),41 promiseAllStatementTransformer(converterOptions),42 consoleLogStatementTransformer(converterOptions),43 newDateTransformer,44 callStatementTransformer(converterOptions),45 nullCoalescingStatementTransformer(converterOptions),46 deployTimeStatementTransformer,47 literalExpressionTransformer,48 stringTemplateTransformer,49 stringConversionTransformer,50 throwStatementTransformer(converterOptions),51 whileStatementTransformer(converterOptions),52 doWhileStatementTransformer(converterOptions),53 forOfStatementTransformer(converterOptions),54 ];...

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NodeTransformer.ts

Source:NodeTransformer.ts Github

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1export enum NodeTransformer {2 BooleanLiteralTransformer = 'BooleanLiteralTransformer',3 BlockStatementControlFlowTransformer = 'BlockStatementControlFlowTransformer',4 BlockStatementSimplifyTransformer = 'BlockStatementSimplifyTransformer',5 ClassFieldTransformer = 'ClassFieldTransformer',6 CommentsTransformer = 'CommentsTransformer',7 CustomCodeHelpersTransformer = 'CustomCodeHelpersTransformer',8 DeadCodeInjectionIdentifiersTransformer = 'DeadCodeInjectionIdentifiersTransformer',9 DeadCodeInjectionTransformer = 'DeadCodeInjectionTransformer',10 DirectivePlacementTransformer = 'DirectivePlacementTransformer',11 EscapeSequenceTransformer = 'EscapeSequenceTransformer',12 EvalCallExpressionTransformer = 'EvalCallExpressionTransformer',13 ExportSpecifierTransformer = 'ExportSpecifierTransformer',14 ExpressionStatementsMergeTransformer = 'ExpressionStatementsMergeTransformer',15 FunctionControlFlowTransformer = 'FunctionControlFlowTransformer',16 IfStatementSimplifyTransformer = 'IfStatementSimplifyTransformer',17 LabeledStatementTransformer = 'LabeledStatementTransformer',18 MemberExpressionTransformer = 'MemberExpressionTransformer',19 MetadataTransformer = 'MetadataTransformer',20 NumberLiteralTransformer = 'NumberLiteralTransformer',21 NumberToNumericalExpressionTransformer = 'NumberToNumericalExpressionTransformer',22 ObfuscatingGuardsTransformer = 'ObfuscatingGuardsTransformer',23 ObjectExpressionKeysTransformer = 'ObjectExpressionKeysTransformer',24 ObjectExpressionTransformer = 'ObjectExpressionTransformer',25 ObjectPatternPropertiesTransformer = 'ObjectPatternPropertiesTransformer',26 ParentificationTransformer = 'ParentificationTransformer',27 RenamePropertiesTransformer = 'RenamePropertiesTransformer',28 ScopeIdentifiersTransformer = 'ScopeIdentifiersTransformer',29 ScopeThroughIdentifiersTransformer = 'ScopeThroughIdentifiersTransformer',30 SplitStringTransformer = 'SplitStringTransformer',31 StringArrayTransformer = 'StringArrayTransformer',32 StringArrayRotateFunctionTransformer = 'StringArrayRotateFunctionTransformer',33 StringArrayScopeCallsWrapperTransformer = 'StringArrayScopeCallsWrapperTransformer',34 TemplateLiteralTransformer = 'TemplateLiteralTransformer',35 VariableDeclarationsMergeTransformer = 'VariableDeclarationsMergeTransformer',36 VariablePreserveTransformer = 'VariablePreserveTransformer'...

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__init__.py

Source:__init__.py Github

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1# Copyright (c) Facebook Inc. and its affiliates.2#3# This source code is licensed under the MIT license found in the4# LICENSE file in the root directory of this source tree.5"""isort:skip_file"""6from .transformer_config import (7 TransformerConfig,8 DEFAULT_MAX_SOURCE_POSITIONS,9 DEFAULT_MAX_TARGET_POSITIONS,10 DEFAULT_MIN_PARAMS_TO_WRAP,11)12from .transformer_decoder import TransformerDecoder, TransformerDecoderBase, Linear13from .transformer_encoder import TransformerEncoder, TransformerEncoderBase14from .transformer_legacy import (15 TransformerModel,16 base_architecture,17 tiny_architecture,18 transformer_iwslt_de_en,19 transformer_wmt_en_de,20 transformer_vaswani_wmt_en_de_big,21 transformer_vaswani_wmt_en_fr_big,22 transformer_wmt_en_de_big,23 transformer_wmt_en_de_big_t2t,24)25from .transformer_base import TransformerModelBase, Embedding26__all__ = [27 "TransformerModelBase",28 "TransformerConfig",29 "TransformerDecoder",30 "TransformerDecoderBase",31 "TransformerEncoder",32 "TransformerEncoderBase",33 "TransformerModel",34 "Embedding",35 "Linear",36 "base_architecture",37 "tiny_architecture",38 "transformer_iwslt_de_en",39 "transformer_wmt_en_de",40 "transformer_vaswani_wmt_en_de_big",41 "transformer_vaswani_wmt_en_fr_big",42 "transformer_wmt_en_de_big",43 "transformer_wmt_en_de_big_t2t",44 "DEFAULT_MAX_SOURCE_POSITIONS",45 "DEFAULT_MAX_TARGET_POSITIONS",46 "DEFAULT_MIN_PARAMS_TO_WRAP",...

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Using AI Code Generation

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1import { transformer } from 'ts-auto-mock/transformer';2import { transformer } from 'ts-auto-mock/transformer';3import { transformer } from 'ts-auto-mock/transformer';4import { transformer } from 'ts-auto-mock/transformer';5import { transformer } from 'ts-auto-mock/transformer';6import { transformer } from 'ts-auto-mock/transformer';7import { transformer } from 'ts-auto-mock/transformer';8import { transformer } from 'ts-auto-mock/transformer';9import { transformer } from 'ts-auto-mock/transformer';10import { transformer } from 'ts-auto-mock/transformer';11import { transformer } from 'ts-auto-mock/transformer';12import { transformer } from 'ts-auto-mock/transformer';13import { transformer } from 'ts-auto-mock/transformer';14import { transformer } from 'ts-auto-mock/transformer';15import { transformer } from 'ts-auto-mock/transformer';16import { transformer } from 'ts-auto-mock/transformer';17import { transformer } from 'ts-auto-mock/transformer';

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Using AI Code Generation

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1import { transformer } from 'ts-auto-mock/transformer';2export function transform(source: string): string {3 return transformer(source);4}5import { transformer } from 'ts-auto-mock/transformer';6export function transform(source: string): string {7 return transformer(source);8}9import { transformer } from 'ts-auto-mock/transformer';10export function transform(source: string): string {11 return transformer(source);12}13import { transformer } from 'ts-auto-mock/transformer';14export function transform(source: string): string {15 return transformer(source);16}17import { transformer } from 'ts-auto-mock/transformer';18export function transform(source: string): string {19 return transformer(source);20}21import { transformer } from 'ts-auto-mock/transformer';22export function transform(source: string): string {23 return transformer(source);24}25import { transformer } from 'ts-auto-mock/transformer';26export function transform(source: string): string {27 return transformer(source);28}29import { transformer } from 'ts-auto-mock/transformer';30export function transform(source: string): string {31 return transformer(source);32}

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Using AI Code Generation

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1import {transform} from 'ts-auto-mock/transformer';2import {transformer} from 'ts-auto-mock/transformer';3import * as ts from 'typescript';4const myTransformer = transformer({5});6const sourceFile = ts.createSourceFile(7 import {transform} from 'ts-auto-mock/transformer';8 import {transformer} from 'ts-auto-mock/transformer';9 import * as ts from 'typescript';10);11const result = ts.transform(sourceFile, [myTransformer]);12const transformedSourceFile = result.transformed[0];13const printer = ts.createPrinter();14const output = printer.printFile(transformedSourceFile);15console.log(output);16import {transform} from 'ts-auto-mock/transformer';17import {transformer} from 'ts-auto-mock/transformer';18import * as ts from 'typescript';19const myTransformer = transformer({20});21const sourceFile = ts.createSourceFile(22 import {transform} from 'ts-auto-mock/transformer';23 import {transformer} from 'ts-auto-mock/transformer';24 import * as ts from 'typescript';25);26const result = ts.transform(sourceFile, [myTransformer]);27const transformedSourceFile = result.transformed[0];28const printer = ts.createPrinter();29const output = printer.printFile(transformedSourceFile);30console.log(output);31import {transform} from 'ts-auto-mock/transformer';32import {transformer} from 'ts-auto-mock/transformer';33import * as ts from 'typescript';34const myTransformer = transformer({35});36const sourceFile = ts.createSourceFile(37 import {transform} from 'ts-auto-mock/transformer';38 import {transformer} from 'ts-auto-mock

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Using AI Code Generation

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1import { transformer } from 'ts-auto-mock/transformer';2import { createMock } from 'ts-auto-mock';3const mock = createMock<MyInterface>();4const tsAutoMockTransformer = transformer(mock);5export = tsAutoMockTransformer;6import { transformer } from 'ts-auto-mock/transformer';7import { createMock } from 'ts-auto-mock';8const mock = createMock<MyInterface>();9const tsAutoMockTransformer = transformer(mock);10export = tsAutoMockTransformer;11{12 "compilerOptions": {13 { "transform": "test1.js" },14 { "transform": "test2.js" }15 }16}

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