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How to use transformer method in pandera

Best Python code snippet using pandera_python

vggtransformer.py

Source:vggtransformer.py

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    )...

FromOptionsTransformer.js

Source:FromOptionsTransformer.js

...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      });...

QueryBuilderTest.js

Source:QueryBuilderTest.js

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  }...

ConvertingTransformersModule.ts

Source:ConvertingTransformersModule.ts

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            ));...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.